insert '' by 'CABRERA-ARTEGAGA'
JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER
JAVIER CABRERA-ARTEAGA
insert '' by 'JAVIER'
JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER CABRERA-ARTEGAGA JAVIER
JAVIER CABRERA-ARTEAGA
for
replace 'for' by 'For'
For WebAssembly.
for WebAssembly
for
This sentence does not start with an uppercase letter.
for WebAssembly
insert '' by 'software.'
Software is a tool that allows you to download and install software.
Software
insert '' by 'install'
Software is a tool that allows you to download and install software.
Software
insert '' by 'and'
Software is a tool that allows you to download and install software.
Software
insert '' by 'download'
Software is a tool that allows you to download and install software.
Software
insert '' by 'to'
Software is a tool that allows you to download and install software.
Software
insert '' by 'you'
Software is a tool that allows you to download and install software.
Software
insert '' by 'allows'
Software is a tool that allows you to download and install software.
Software
insert '' by 'that'
Software is a tool that allows you to download and install software.
Software
insert '' by 'tool'
Software is a tool that allows you to download and install software.
Software
insert '' by 'a'
Software is a tool that allows you to download and install software.
Software
insert '' by 'is'
Software is a tool that allows you to download and install software.
Software
insert '' by 'Collect'
Al , ak” & KTH Nata 2 $8 OCH KONST %> o) Q Bios. Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collect
al , ak” & KTH Nata §2 $8 OCH KONST %> o) Q Bios. Col
insert '' by 'Collectible'
Al , ak” & KTH Nata 2 $8 OCH KONST %> o) Q Bios. Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collect
al , ak” & KTH Nata §2 $8 OCH KONST %> o) Q Bios. Col
Col
replace 'Col' by 'Collectible'
Al , ak” & KTH Nata 2 $8 OCH KONST %> o) Q Bios. Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collect
al , ak” & KTH Nata §2 $8 OCH KONST %> o) Q Bios. Col
§2
replace '§2' by '2'
Al , ak” & KTH Nata 2 $8 OCH KONST %> o) Q Bios. Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collect
al , ak” & KTH Nata §2 $8 OCH KONST %> o) Q Bios. Col
al
replace 'al' by 'Al'
Al , ak” & KTH Nata 2 $8 OCH KONST %> o) Q Bios. Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collectible Collect
al , ak” & KTH Nata §2 $8 OCH KONST %> o) Q Bios. Col
al
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al , ak” & KTH Nata §2 $8 OCH KONST %> o) Q Bios. Col
2024
replace '2024' by '2024.'
Stockholm, Sweden, March 2024.
Stockholm, Sweden, March 2024
2024
delete '2024' by ''
Javier Cabrera-Arteaga, March 7th 2024.
© Javier Cabrera-Arteaga , March 7th 2024
7th
delete '7th' by ''
Javier Cabrera-Arteaga, March 7th 2024.
© Javier Cabrera-Arteaga , March 7th 2024
March
replace 'March' by '2024.'
Javier Cabrera-Arteaga, March 7th 2024.
© Javier Cabrera-Arteaga , March 7th 2024
,
replace ',' by '7th'
Javier Cabrera-Arteaga, March 7th 2024.
© Javier Cabrera-Arteaga , March 7th 2024
Javier
replace 'Javier' by 'Cabrera-Arteaga,'
Javier Cabrera-Arteaga, March 7th 2024.
© Javier Cabrera-Arteaga , March 7th 2024
©
replace '©' by 'Javier'
Javier Cabrera-Arteaga, March 7th 2024.
© Javier Cabrera-Arteaga , March 7th 2024
978-91-8040-822-6
replace '978-91-8040-822-6' by '978-91-8040-822-6.'
TRITA-EECS-AVL-2024:10 ISBN 978-91-8040-822-6.
TRITA-EECS-AVL-2024:10 ISBN 978-91-8040-822-6
AB
replace 'AB' by 'AB.'
Tryck: Universitetsservice US AB.
Tryck: Universitetsservice US AB
insert '' by 'Abstract:'
Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract: Abstract
Abstract
Diversification
replace 'Diversification' by 'diversification'
This work aims to harden the security within the WebAssembly ecosystem through the introduction of software diversification methods and tools. Software Diversification is a strategy designed to augment the costs of exploiting vulnerabilities by making software less predictable. The predictability within ecosystems can be diminished by automatically generating different, yet functionally equivalent, program variants. These variants strengthen observable properties that are typically used to launch attacks, and in many instances, can eliminate such vulnerabilities.
This work aims to harden the security within the WebAssembly ecosystem through the introduction of Software Diversification methods and tools. Software Diversification is a strategy designed to augment the costs of exploiting vulnerabilities by making software less predictable. The predictability within ecosystems can be diminished by automatically generating different, yet functionally equivalent, program variants. These variants strengthen observable properties that are typically used to launch attacks, and in many instances, can eliminate such vulnerabilities.
Software
replace 'Software' by 'software'
This work aims to harden the security within the WebAssembly ecosystem through the introduction of software diversification methods and tools. Software Diversification is a strategy designed to augment the costs of exploiting vulnerabilities by making software less predictable. The predictability within ecosystems can be diminished by automatically generating different, yet functionally equivalent, program variants. These variants strengthen observable properties that are typically used to launch attacks, and in many instances, can eliminate such vulnerabilities.
This work aims to harden the security within the WebAssembly ecosystem through the introduction of Software Diversification methods and tools. Software Diversification is a strategy designed to augment the costs of exploiting vulnerabilities by making software less predictable. The predictability within ecosystems can be diminished by automatically generating different, yet functionally equivalent, program variants. These variants strengthen observable properties that are typically used to launch attacks, and in many instances, can eliminate such vulnerabilities.
tools)
replace 'tools)' by 'tools:'
This work introduces three tools: CROW, MEWE as compiler-based approaches, and WASM-MUTATE as a binary-based approach. Each tool has been specifically designed to tackle a unique facet of Software Diversification. We present empirical evidence demonstrating the potential application of our Software Diversification methods to WebAssembly programs in two distinct ways: Offensive and Defensive Software Diversification. Our research into Offensive Software Diversification in WebAssembly unveils potential paths for enhancing the detection of WebAssembly malware. On the other hand, our experiments in Defensive Software Diversification show that WebAssembly programs can be hardened against side-channel attacks, specifically the Spectre attack.
This work introduces three tools) CROW, MEWE as compiler-based approaches, and WASM-MUTATE as a binary-based approach. Each tool has been specifically designed to tackle a unique facet of Software Diversification. We present empirical evidence demonstrating the potential application of our Software Diversification methods to WebAssembly programs in two distinct ways: Offensive and Defensive Software Diversification. Our research into Offensive Software Diversification in WebAssembly unveils potential paths for enhancing the detection of WebAssembly malware. On the other hand, our experiments in Defensive Software Diversification show that WebAssembly programs can be hardened against side-channel attacks, specifically the Spectre attack.
)
Unpaired symbol: ‘(’ seems to be missing
This work introduces three tools) CROW, MEWE as compiler-based approaches, and WASM-MUTATE as a binary-based approach. Each tool has been specifically designed to tackle a unique facet of Software Diversification. We present empirical evidence demonstrating the potential application of our Software Diversification methods to WebAssembly programs in two distinct ways: Offensive and Defensive Software Diversification. Our research into Offensive Software Diversification in WebAssembly unveils potential paths for enhancing the detection of WebAssembly malware. On the other hand, our experiments in Defensive Software Diversification show that WebAssembly programs can be hardened against side-channel attacks, specifically the Spectre attack.
Side-Channels
replace 'Side-Channels' by 'Side-Channels.'
Keywords: WebAssembly, Software Diversification, Side-Channels.
Keywords: WebAssembly, Software Diversification, Side-Channels
som
replace 'som' by 'so'
Denna avhandling syftar till att starka sikerheten inom WebAssembly-ekosystemet genom inforandet av metoder och verktyg for mjukvarudiversifiering. Mjukvarudiversifiering ar en strategi som ar utformad for att dka kostnaderna for att exploatera sarbarheter genom att gora programvaran of6érutsagbar. Fdérutsagbarheten inom ekosystem kan minskas genom att automatiskt generera olika programvaruvarianter. Dessa varianter forstarker observerbara egenskaper so vanligtvis anvands for att starta attacker och kan i manga fall helt eliminera sadana sarbarheter.
Denna avhandling syftar till att starka sikerheten inom WebAssembly-ekosystemet genom inforandet av metoder och verktyg for mjukvarudiversifiering. Mjukvarudiversifiering ar en strategi som ar utformad for att dka kostnaderna for att exploatera sarbarheter genom att gora programvaran of6érutsagbar. Fdérutsagbarheten inom ekosystem kan minskas genom att automatiskt generera olika programvaruvarianter. Dessa varianter forstarker observerbara egenskaper som vanligtvis anvands for att starta attacker och kan i manga fall helt eliminera sadana sarbarheter.
Webbsideresurser,
replace 'Webbsideresurser,' by 'Websideresurser,'
Websideresurser, inklusive de som innehaller exekverbar WebAssembly, skickas i en modern digital kontext huvudsakligen fran centraliserade datacenter. Tusentals edge-enheter, i samarbete med webblasarklienter, kor miljontals identiska WebAssembly-instantieringar varje sekund. Detta fenomen skapar ett hdogst forutsagbart ekosystem, dar potentiella angripare kan forutse beteenden antingen i webblasare eller backend-noder. En sadan forutsagbarhet dkar potentialen for sarbarheter inom dessa ekosystem och Oppnar doérren f6r sidkanal-och minnesattacker med stor paverkan. Till exempel kan en brist i en webblasare, framkallad av ett defekt WebAssembly-program, ha potential att paverka miljontals anvandare.
Webbsideresurser, inklusive de som innehaller exekverbar WebAssembly, skickas i en modern digital kontext huvudsakligen fran centraliserade datacenter. Tusentals edge-enheter, i samarbete med webblasarklienter, kor miljontals identiska WebAssembly-instantieringar varje sekund. Detta fenomen skapar ett hdogst forutsagbart ekosystem, dar potentiella angripare kan forutse beteenden antingen i webblasare eller backend-noder. En sadan forutsagbarhet dkar potentialen for sarbarheter inom dessa ekosystem och Oppnar doérren f6r sidkanal-och minnesattacker med stor paverkan. Till exempel kan en brist i en webblasare, framkallad av ett defekt WebAssembly-program, ha potential att paverka miljontals anvandare.
av
delete 'av' by ''
WebAssembly, nu det fjarde officiallt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.
WebAssembly, nu det fjarde officiellt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. Dessutom har WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former av attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.
har
delete 'har' by ''
WebAssembly, nu det fjarde officiallt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.
WebAssembly, nu det fjarde officiellt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. Dessutom har WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former av attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.
Dessutom
delete 'Dessutom' by ''
WebAssembly, nu det fjarde officiallt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.
WebAssembly, nu det fjarde officiellt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. Dessutom har WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former av attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.
officiellt
replace 'officiellt' by 'officiallt'
WebAssembly, nu det fjarde officiallt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.
WebAssembly, nu det fjarde officiellt erkénda webbspraket, gor det mojligt for webblasare att portera nativa applikationer till webben. Dessutom har WebAssembly utvecklats till en vasentlig komponent f6r backend-scenarier sdsom molntjanster och edge-tjénster. Darmed anvands WebAssembly i en méangd olika applikationer, daribland webblasare, blockchain och molntjanster. Trots sitt fokus pa sakerhet fran dess design till dess specifikation ar WebAssembly fortfarande mottagligt for olika former av attacker, sasom minneskorruption och sidokanalattacker. Dessutom har WebAssembly manipulerats for att sprida skadlig programvara, sarskilt otillaten cryptobrytning i webblasare.
defensiv
replace 'defensiv' by 'defensive'
Detta arbete introducerar tre verktyg: CROW, MEWE och WASM-MUTATE. Varje verktyg har utformats specifikt for att hantera en unik aspekt av mjukvarudiversifiering. Vi presenterar empiriska bevis som visar pa potentialen for tillampning av vara metoder for mjukvarudiversifiering av WebAssembly-program pa tva distinkta satt: offensive och defensive mjukvarudiversifiering. Var forskning om offensive mjukvarudiversifiering i WebAssembly avsléjar potentialla vagar for att forbattra upptackten av WebAssembly-malware. A andra sidan visar vara experiment inom defensive mjukvarudiversifiering att WebAssembly-program kan hardas mot sidokanalattacker, sarskilt Spectre-attacken.
Detta arbete introducerar tre verktyg: CROW, MEWE och WASM-MUTATE. Varje verktyg har utformats specifikt for att hantera en unik aspekt av mjukvarudiversifiering. Vi presenterar empiriska bevis som visar pa potentialen for tillampning av vara metoder for mjukvarudiversifiering av WebAssembly-program pa tva distinkta satt: offensiv och defensiv mjukvarudiversifiering. Var forskning om offensiv mjukvarudiversifiering i WebAssembly avsléjar potentiella vagar for att forbattra upptackten av WebAssembly-malware. A andra sidan visar vara experiment inom defensiv mjukvarudiversifiering att WebAssembly-program kan hardas mot sidokanalattacker, sarskilt Spectre-attacken.
potentiella
replace 'potentiella' by 'potentialla'
Detta arbete introducerar tre verktyg: CROW, MEWE och WASM-MUTATE. Varje verktyg har utformats specifikt for att hantera en unik aspekt av mjukvarudiversifiering. Vi presenterar empiriska bevis som visar pa potentialen for tillampning av vara metoder for mjukvarudiversifiering av WebAssembly-program pa tva distinkta satt: offensive och defensive mjukvarudiversifiering. Var forskning om offensive mjukvarudiversifiering i WebAssembly avsléjar potentialla vagar for att forbattra upptackten av WebAssembly-malware. A andra sidan visar vara experiment inom defensive mjukvarudiversifiering att WebAssembly-program kan hardas mot sidokanalattacker, sarskilt Spectre-attacken.
Detta arbete introducerar tre verktyg: CROW, MEWE och WASM-MUTATE. Varje verktyg har utformats specifikt for att hantera en unik aspekt av mjukvarudiversifiering. Vi presenterar empiriska bevis som visar pa potentialen for tillampning av vara metoder for mjukvarudiversifiering av WebAssembly-program pa tva distinkta satt: offensiv och defensiv mjukvarudiversifiering. Var forskning om offensiv mjukvarudiversifiering i WebAssembly avsléjar potentiella vagar for att forbattra upptackten av WebAssembly-malware. A andra sidan visar vara experiment inom defensiv mjukvarudiversifiering att WebAssembly-program kan hardas mot sidokanalattacker, sarskilt Spectre-attacken.
offensiv
replace 'offensiv' by 'offensive'
Detta arbete introducerar tre verktyg: CROW, MEWE och WASM-MUTATE. Varje verktyg har utformats specifikt for att hantera en unik aspekt av mjukvarudiversifiering. Vi presenterar empiriska bevis som visar pa potentialen for tillampning av vara metoder for mjukvarudiversifiering av WebAssembly-program pa tva distinkta satt: offensive och defensive mjukvarudiversifiering. Var forskning om offensive mjukvarudiversifiering i WebAssembly avsléjar potentialla vagar for att forbattra upptackten av WebAssembly-malware. A andra sidan visar vara experiment inom defensive mjukvarudiversifiering att WebAssembly-program kan hardas mot sidokanalattacker, sarskilt Spectre-attacken.
Detta arbete introducerar tre verktyg: CROW, MEWE och WASM-MUTATE. Varje verktyg har utformats specifikt for att hantera en unik aspekt av mjukvarudiversifiering. Vi presenterar empiriska bevis som visar pa potentialen for tillampning av vara metoder for mjukvarudiversifiering av WebAssembly-program pa tva distinkta satt: offensiv och defensiv mjukvarudiversifiering. Var forskning om offensiv mjukvarudiversifiering i WebAssembly avsléjar potentiella vagar for att forbattra upptackten av WebAssembly-malware. A andra sidan visar vara experiment inom defensiv mjukvarudiversifiering att WebAssembly-program kan hardas mot sidokanalattacker, sarskilt Spectre-attacken.
insert '' by '230'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
2021.
replace '2021.' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
https://doi.org/10.14722/madweb.
replace 'https://doi.org/10.14722/madweb.' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
pages
replace 'pages' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
12
replace '12' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
2021),
replace '2021),' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
(MADWeb
replace '(MADWeb' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Web
replace 'Web' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
the
replace 'the' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
for
replace 'for' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Defenses
replace 'Defenses' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
and
replace 'and' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Attacks,
replace 'Attacks,' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Measurements,
replace 'Measurements,' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
on
replace 'on' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Workshop
replace 'Workshop' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Martin
replace 'Martin' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Baudry,
replace 'Baudry,' by '23004'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Benoit
replace 'Benoit' by '2021.'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Vera-Pérez,
replace 'Vera-Pérez,' by 'https://doi.org/10.14722/madweb.'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Oscar
replace 'Oscar' by 'pages'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Floros,
replace 'Floros,' by '12'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Orestis
replace 'Orestis' by '2021),'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Cabrera-Arteaga,
replace 'Cabrera-Arteaga,' by '(MADWeb'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Javier
replace 'Javier' by 'Web'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Defenses'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'and'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Attacks,'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Measurements,'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'on'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Workshop'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Monperrus'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Martin'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Baudry,'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Benoit'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Vera-Pérez,'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Oscar'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Floros,'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Orestis'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Cabrera-Arteaga,'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Javier'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'WebAssembly'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Diversification
replace 'Diversification' by 'for'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Code
replace 'Code' by 'Diversification'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
CROW:
replace 'CROW:' by 'Code'
4. Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
4. CROW: Code Diversification for WebAssembly Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by '3564007)'
3. Multi-Variant Execution at the Edge Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007)
3. Multi-Variant Execution at the Edge Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007
insert '' by '-'
3. Multi-Variant Execution at the Edge Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007)
3. Multi-Variant Execution at the Edge Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007
3564007
replace '3564007' by '3564007)'
3. Multi-Variant Execution at the Edge Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007)
3. Multi-Variant Execution at the Edge Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007
00324
delete '00324' by ''
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
https://www.sciencedirect.com/science/article/pii/S01674048240
replace 'https://www.sciencedirect.com/science/article/pii/S01674048240' by '00324.'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
pages
replace 'pages' by 'https://www.sciencedirect.com/science/article/pii/S01674048240'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
20
replace '20' by 'pages'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
2024,
replace '2024,' by '20'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
Security,
replace 'Security,' by '2024,'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
&
replace '&' by 'Security,'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
Computers
replace 'Computers' by '&'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
Benoit
replace 'Benoit' by 'Baudry'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
Monperrus,
replace 'Monperrus,' by 'Benoit'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
Martin
replace 'Martin' by 'Monperrus,'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
Fitzgerald,
replace 'Fitzgerald,' by 'Martin'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
Nicholas
replace 'Nicholas' by 'Fitzgerald,'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
Cabrera-Arteaga,
replace 'Cabrera-Arteaga,' by 'Nicholas'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
Javier
replace 'Javier' by 'Cabrera-Arteaga,'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
for
replace 'for' by 'WebAssembly'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
Diversification
replace 'Diversification' by 'for'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
Binary
replace 'Binary' by 'Diversification'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
Effective
replace 'Effective' by 'Binary'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
and
replace 'and' by 'Effective'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
Fast
replace 'Fast' by 'and'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
WASM-MUTATE:
replace 'WASM-MUTATE:' by 'Fast'
2. Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
2. WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
02067
replace '02067' by '02067.'
1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067.
1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga,Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067
insert '' by 'Tim'
1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067.
1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga,Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067
Cabrera-Arteaga,Tim
replace 'Cabrera-Arteaga,Tim' by 'Cabrera-Arteaga,'
1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067.
1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga,Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067
,Tim
Put a space after the comma.
1. WebAssembly Diversification for Malware Evasion Javier Cabrera-Arteaga,Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages https://www.sciencedirect.com/science/article/pii/S01674048230 02067
PAPERS
replace 'PAPERS' by 'PAPERS.'
LIST OF PAPERS.
LIST OF PAPERS
https://doi.org/10.1145/3397537.3397567
replace 'https://doi.org/10.1145/3397537.3397567' by 'https://doi.org/10.1145/3397537.3397567.'
5. Superoptimization of WebAssembly Bytecode Javier Cabrera-Arteaga, Shrinish Donde, Jian Gu, Orestis Floros, Lucas Satabin, Benoit Baudry, Martin Monperrus Conference Companion of the 4th International Conference on Art, Science, and Engineering of Programming (Programming 2021), MoreVMs, 4 pages https://doi.org/10.1145/3397537.3397567.
5. Superoptimization of WebAssembly Bytecode Javier Cabrera-Arteaga, Shrinish Donde, Jian Gu, Orestis Floros, Lucas Satabin, Benoit Baudry, Martin Monperrus Conference Companion of the 4th International Conference on Art, Science, and Engineering of Programming (Programming 2021), MoreVMs, 4 pages https://doi.org/10.1145/3397537.3397567
insert '' by 'all'
I would like to extend my gratitude to my supervisors, Benoit Baudry and Martin Monperrus. Their guidance has been a constant, ensuring that I never felt lost. Their support extended beyond professional advice, blossoming into a genuine friendship. There were times when I might have been challenging to work with, but I deeply appreciate your patience and willingness to listen - all that many histories about Cuba could be boring :) -Thank you, Benoit and Martin.
I would like to extend my gratitude to my supervisors, Benoit Baudry and Martin Monperrus. Their guidance has been a constant, ensuring that I never felt lost. Their support extended beyond professional advice, blossoming into a genuine friendship. There were times when I might have been challenging to work with, but I deeply appreciate your patience and willingness to listen -all that many histories about Cuba could be boring :) -Thank you, Benoit and Martin.
-all
replace '-all' by '-'
I would like to extend my gratitude to my supervisors, Benoit Baudry and Martin Monperrus. Their guidance has been a constant, ensuring that I never felt lost. Their support extended beyond professional advice, blossoming into a genuine friendship. There were times when I might have been challenging to work with, but I deeply appreciate your patience and willingness to listen - all that many histories about Cuba could be boring :) -Thank you, Benoit and Martin.
I would like to extend my gratitude to my supervisors, Benoit Baudry and Martin Monperrus. Their guidance has been a constant, ensuring that I never felt lost. Their support extended beyond professional advice, blossoming into a genuine friendship. There were times when I might have been challenging to work with, but I deeply appreciate your patience and willingness to listen -all that many histories about Cuba could be boring :) -Thank you, Benoit and Martin.
insert '' by 'an'
I want to thank the distinguished members of my jury. Professor Sukyoung Ryu, thanks for accepting to take the role of an opponent in my defense. Professor Dilian Gurov, thanks for taking the advance review of this work. Professors, Quentin Stiévenart, Bjorn De Sutter, and Weihang Wang, thank you all for being part of my grading committee and for your invaluable insights.
I want to thank the distinguished members of my jury. Professor Sukyoung Ryu, thanks for accepting to take the role of opponent in my defense. Professor Dilian Gurov, thanks for taking the advance review of this work. Professors, Quentin Stiévenart, Bjorn De Sutter, and Weihang Wang, thank you all for being part of my grading committee and for your invaluable insights.
immeasurably
delete 'immeasurably' by ''
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
contributed
replace 'contributed' by 'immensely'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
has
replace 'has' by 'contributed'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
difficult,
replace 'difficult,' by 'has'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
how
replace 'how' by 'difficult,'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
matter
replace 'matter' by 'how'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
no
replace 'no' by 'matter'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
step,
replace 'step,' by 'no'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Each
replace 'Each' by 'step,'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
challenges.
replace 'challenges.' by 'Each'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
by
replace 'by' by 'challenges.'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
marked
replace 'marked' by 'by'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
journey
replace 'journey' by 'marked'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
a
replace 'a' by 'journey'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
see
replace 'see' by 'a'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
I
replace 'I' by 'see'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
years,
replace 'years,' by 'I'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
five
replace 'five' by 'years,'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
past
replace 'past' by 'five'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
these
replace 'these' by 'past'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
over
replace 'over' by 'these'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
back
replace 'back' by 'over'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
look
replace 'look' by 'back'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
I
replace 'I' by 'look'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
as
replace 'as' by 'I'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Now,
replace 'Now,' by 'as'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
KTH.
replace 'KTH.' by 'Now,'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
at
replace 'at' by 'KTH.'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Engineering
replace 'Engineering' by 'at'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Software
replace 'Software' by 'Engineering'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
in
replace 'in' by 'Software'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
position
replace 'position' by 'in'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Ph.D.
replace 'Ph.D.' by 'Position'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
a
replace 'a' by 'Ph.D.'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
opportunity:
replace 'opportunity:' by 'a'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
life-changing
replace 'life-changing' by 'opportunity:'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
a
replace 'a' by 'life-changing'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
presented
replace 'presented' by 'a'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Vera,
replace 'Vera,' by 'presented'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Luis
replace 'Luis' by 'Vera,'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Oscar
replace 'Oscar' by 'Luis'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
friend,
replace 'friend,' by 'Oscar'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
and
replace 'and' by 'friend,'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
mentor
replace 'mentor' by 'and'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
my
replace 'my' by 'mentor'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
uncertainty,
replace 'uncertainty,' by 'my'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
of
replace 'of' by 'uncertainty,'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
phase
replace 'phase' by 'of'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
this
replace 'this' by 'phase'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
During
replace 'During' by 'this'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
pursuits.
replace 'pursuits.' by 'During'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
academic
replace 'academic' by 'pursuits.'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
my
replace 'my' by 'academic'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
advance
replace 'advance' by 'my'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
to
replace 'to' by 'advance'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
whether
replace 'whether' by 'to'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
of
replace 'of' by 'whether'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
journey
replace 'journey' by 'Journey'
Five years ago, relocating to Sweden for a Ph.D. Journey seemed unimaginable. At that time, I was grappling with the decision whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. Position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immensely to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
Five years ago, relocating to Sweden for a Ph.D. journey seemed unimaginable. At that time, I was grappling with the decision of whether to advance my academic pursuits. During this phase of uncertainty, my mentor and friend, Oscar Luis Vera, presented a life-changing opportunity: a Ph.D. position in Software Engineering at KTH. Now, as I look back over these past five years, I see a journey marked by challenges. Each step, no matter how difficult, has contributed immeasurably to my growth and learning. I carry with me a deep sense of accomplishment and no regrets—my heartfelt thanks to Oscar and Ali.
-this
replace '-this' by 'this'
First and foremost, to my beloved wife, Ilena, this journey has been yours as mine.
First and foremost, to my beloved wife, Ilena -this journey has been yours as mine.
Ilena
replace 'Ilena' by 'Ilena,'
First and foremost, to my beloved wife, Ilena, this journey has been yours as mine.
First and foremost, to my beloved wife, Ilena -this journey has been yours as mine.
insert '' by '(SSF)'
These last five years have been supported by the Stiftelsen for Strategisk Forskning (SSF) and the Trustfull project. I would like to thank all my colleagues in this project, especially Professor Musard Balliu for his invaluable insights.
These last five years have been supported by the Stiftelsen for Strategisk Forskning(SSF) and the Trustfull project. I would like to thank all my colleagues in this project, especially Professor Musard Balliu for his invaluable insights.
Forskning(SSF)
replace 'Forskning(SSF)' by 'Forskning'
These last five years have been supported by the Stiftelsen for Strategisk Forskning (SSF) and the Trustfull project. I would like to thank all my colleagues in this project, especially Professor Musard Balliu for his invaluable insights.
These last five years have been supported by the Stiftelsen for Strategisk Forskning(SSF) and the Trustfull project. I would like to thank all my colleagues in this project, especially Professor Musard Balliu for his invaluable insights.
mi
Did you mean “me” or “my”?
Last but never least, to my family in Cuba, especially to “mi abuela Mary”. Family values led me to this successful journey.
outline
replace 'outline' by 'outline:'
Introduction 1.1 1.2 1.3 1.4 1.5 1.6 WebAssembly Predictability in WebAssembly ecosystems Problem statements Approach: Software Diversification Summary of research papers Thesis outline:
Introduction 1.1 1.2 1.3 1.4 1.5 1.6 WebAssembly Predictability in WebAssembly ecosystems Problem statements Approach: Software Diversification Summary of research papers Thesis outline
Thesis
replace 'Thesis' by 'thesis.'
I thesis.
I Thesis
insert '' by 'Contents:'
Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents: Contents
Contents
Papers
replace 'Papers' by 'Papers.'
List of Papers.
List of Papers
Diversification
replace 'Diversification' by 'Divers'
Background and state of the art 2.1 WebAssembly 2.1.1 From source code to WebAssembly 2.1.2 WebAssembly’s binary format 2.1.3 WebAssembly’s runtime 2.1.4 WebAssembly’s control-flow 2.1.5 Security and reliability for WebAssembly 2.1.6 Open challenges 2.2 Software diversification 2.2.1 Automatic generation of software variants 2.2.2 Equivalence Checking 2.2.3 Variants deployment 2.2.4 Measuring Software Divers
Background and state of the art 2.1 WebAssembly 2.1.1 From source code to WebAssembly 2.1.2 WebAssembly’s binary format 2.1.3 WebAssembly’s runtime 2.1.4 WebAssembly’s control-flow 2.1.5 Security and reliability for WebAssembly 2.1.6 Open challenges 2.2 Software diversification 2.2.1 Automatic generation of software variants 2.2.2 Equivalence Checking 2.2.3 Variants deployment 2.2.4 Measuring Software Diversification
Defensive
delete 'Defensive' by ''
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
Results
delete 'Results' by ''
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
Methodology
delete 'Methodology' by ''
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
evasion
delete 'evasion' by ''
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
defense
delete 'defense' by ''
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
Malware
delete 'Malware' by ''
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
Diversification:
delete 'Diversification:' by ''
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
Assessing Software Diversification for WebAssembly 4.1 4.1.1 4.1.2 4.1.3 4.2 4.2.1 4.2.2 4.2.3 4.3 Offensive Diversification: Malware evasion Cryptojacking defense evasion Methodology Results Defensive Diversification: speculative side-channel protection Threat model: speculative side-channel attacks Methodology Results Conclusions
applications
delete 'applications' by ''
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
Security
delete 'Security' by ''
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
and
delete 'and' by ''
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
MEWE,
delete 'MEWE,' by ''
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
CROW,
delete 'CROW,' by ''
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
Comparing
delete 'Comparing' by ''
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
Exemplifying
delete 'Exemplifying' by ''
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
traversal
delete 'traversal' by ''
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
E-Graph
delete 'E-Graph' by ''
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
Rules
delete 'Rules' by ''
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
Rewriting
delete 'Rewriting' by ''
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
for
delete 'for' by ''
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
Diversification
delete 'Diversification' by ''
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
Binary
replace 'Binary' by 'WebAssembl'
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
Effective
replace 'Effective' by 'for'
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
and
replace 'and' by 'Diversification'
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
Fast
replace 'Fast' by 'Binary'
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
WASM-MUTATE:
replace 'WASM-MUTATE:' by 'Effective'
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
binary
replace 'binary' by 'and'
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
a
replace 'a' by 'WASM-MUTATE:'
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
Multi-variant
replace 'Multi-variant' by 'Multivariant'
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multivariant Execution for WebAssembly Multivariant call graph Exemplifying WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembl
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
WebAssembly WebAssembly
Possible typo: you repeated a word
Automatic Software Diversification for WebAssembly 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.4.1 CROW: Code Randomization of WebAssembly Enumerative synthesis Constant inferring Exemplifying CROW MEWE: Multi-variant Execution for WebAssembly Multivariant call graph Exemplifying a Multivariant binary WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly WebAssembly Rewriting Rules E-Graph traversal Exemplifying WASM-MUTATE Comparing CROW, MEWE, and WASM-MUTATE Security applications
Diversification
replace 'Diversification' by 'diversification.'
2.2.5 Offensive or defensive assessment of diversification 2.3 Open challenges for software diversification.
2.2.5 Offensive or Defensive assessment of diversification 2.3 Open challenges for Software Diversification
Software
replace 'Software' by 'software'
2.2.5 Offensive or defensive assessment of diversification 2.3 Open challenges for software diversification.
2.2.5 Offensive or Defensive assessment of diversification 2.3 Open challenges for Software Diversification
Defensive
replace 'Defensive' by 'defensive'
2.2.5 Offensive or defensive assessment of diversification 2.3 Open challenges for software diversification.
2.2.5 Offensive or Defensive assessment of diversification 2.3 Open challenges for Software Diversification
Diversification
replace 'Diversification' by 'Diversification.'
Conclusions and Future Work 5.1 5.2 5.3 5.3.1 5.3.2 5.3.3 Summary of technical contributions Key results of the thesis Future Work Data augmentation for Machine Learning on WebAssembly programs Improving WebAssembly malware detection via canonicalization Oneshot Diversification.
Conclusions and Future Work 5.1 5.2 5.3 5.3.1 5.3.2 5.3.3 Summary of technical contributions Key results of the thesis Future Work Data augmentation for Machine Learning on WebAssembly programs Improving WebAssembly malware detection via canonicalization Oneshot Diversification
insert '' by '123'
123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123 123
123
121
replace '121' by '121.'
121.
121
Edge
replace 'Edge' by 'Edge.'
Multi-Variant Execution at the Edge.
Multi-Variant Execution at the Edge
119
replace '119' by '119.'
119.
119
117
replace '117' by '117.'
117.
117
115
replace '115' by '115.'
WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly 115.
WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly 115
Evasion
replace 'Evasion' by 'Evasion.'
WebAssembly Diversification for Malware Evasion.
WebAssembly Diversification for Malware Evasion
113
replace '113' by '113.'
113.
113
109
replace '109' by '109.'
109.
109
papers
replace 'papers' by 'papers.'
II Included papers.
II Included papers
References
replace 'References' by 'References.'
References.
References
Traces
replace 'Traces' by 'Traces.'
Scalable Comparison of JavaScript V8 Bytecode Traces.
Scalable Comparison of JavaScript V8 Bytecode Traces
I
delete 'I' by ''
Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part
Part I
insert '' by '-'
Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part
Part I
insert '' by 'I'
Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part
Part I
insert '' by 'Part'
Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part I - Part
Part I
insert '' by '-'
Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis
Thesis
insert '' by 'Thesis'
Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis - Thesis
Thesis
-all
delete '-all' by ''
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
marriage
replace 'marriage' by 'all'
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
happy
replace 'happy' by '-'
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
a
replace 'a' by 'marriage'
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
punishment;
replace 'punishment;' by 'happy'
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
of
replace 'of' by 'a'
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
dénouement
replace 'dénouement' by 'punishment;'
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
a
replace 'a' by 'of'
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
not;
replace 'not;' by 'denouement'
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
what
replace 'what' by 'a'
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
or
replace 'or' by 'not;'
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
show;
replace 'show;' by 'what'
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or what not; a denouement of punishment; a happy marriage - all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
Jealous stepmother and sisters; magical aid by a beast; a marriage won by gifts magically provided; a bird revealing a secret; a recognition by aid of a ring; or show; or what not; a dénouement of punishment; a happy marriage -all those things, which in sequence, make up Cinderella, may and do occur in an incalculable number of other combinations.
its
delete 'its' by ''
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
At
delete 'At' by ''
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
[2].
replace '[2].' by 'best.'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
1990
replace '1990' by 'its'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
in
replace 'in' by 'At'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
appearance
replace 'appearance' by '[2].'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
its
replace 'its' by '1990'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
made
replace 'made' by 'in'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
Nexus,
replace 'Nexus,' by 'appearance'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
browser,
replace 'browser,' by 'its'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
web
replace 'web' by 'made'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
first
replace 'first' by 'Nexus,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
TT
replace 'TT' by 'browser,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
renderers.
replace 'renderers.' by 'web'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
document
replace 'document' by 'first'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
replace 'PDF' by 'TT's'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
and
replace 'and' by 'renderers.'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
creators,
replace 'creators,' by 'document'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
animation
replace 'animation' by 'PDF'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
players,
replace 'players,' by 'and'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
audio
replace 'audio' by 'creators,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
and
replace 'and' by 'animation'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
video
replace 'video' by 'players,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
as
replace 'as' by 'audio'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
such
replace 'such' by 'and'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
applications,
replace 'applications,' by 'video'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
comprehensive
replace 'comprehensive' by 'as'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
running
replace 'running' by 'such'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
of
replace 'of' by 'applications,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
capable
replace 'capable' by 'comprehensive'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
systems
replace 'systems' by 'running'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
intricate
replace 'intricate' by 'of'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
into
replace 'into' by 'capable'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
evolved
replace 'evolved' by 'systems'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
have
replace 'have' by 'intricate'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
browsers
replace 'browsers' by 'into'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
web
replace 'web' by 'evolved'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
decades,
replace 'decades,' by 'have'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
several
replace 'several' by 'browsers'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
past
replace 'past' by 'web'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
the
replace 'the' by 'decades,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
over
replace 'over' by 'several'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
Consequently,
replace 'Consequently,' by 'past'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
computers.
replace 'computers.' by 'the'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
client
replace 'client' by 'over'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
on
replace 'on' by 'Consequently,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
executed
replace 'executed' by 'computers.'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
is
replace 'is' by 'client'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
which
replace 'which' by 'on'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
code,
replace 'code,' by 'executed'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
JavaScript
replace 'JavaScript' by 'is'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
also
replace 'also' by 'which'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
but
replace 'but' by 'code,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
HTML
replace 'HTML' by 'JavaScript'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
only
replace 'only' by 'also'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
not
replace 'not' by 'but'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
incorporate
replace 'incorporate' by 'HTML'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
pages
replace 'pages' by 'only'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
web
replace 'web' by 'not'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
of
replace 'of' by 'incorporate'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
majority
replace 'majority' by 'pages'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
the
replace 'the' by 'web'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
day,
replace 'day,' by 'of'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
present
replace 'present' by 'majority'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
In
delete 'In' by ''
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
support.
replace 'support.' by 'day,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
JavaScript
replace 'JavaScript' by 'present'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
offer
replace 'offer' by 'the'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
browsers
replace 'browsers' by 'In'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
all
replace 'all' by 'support.'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
Netscape,
replace 'Netscape,' by 'JavaScript'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
of
replace 'of' by 'offer'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
advent
replace 'advent' by 'browsers'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
the
replace 'the' by 'all'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
since
replace 'since' by 'Netscape,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
Significantly,
replace 'Significantly,' by 'of'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
browser.
replace 'browser.' by 'advent'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
web
replace 'web' by 'the'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
a
replace 'a' by 'since'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
with
replace 'with' by 'Significantly,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
device
replace 'device' by 'browser.'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
any
replace 'any' by 'web'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
from
replace 'from' by 'a'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
accessible
replace 'accessible' by 'with'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
are
replace 'are' by 'device'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
and
replace 'and' by 'any'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
up-to-date,
replace 'up-to-date,' by 'from'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
always
replace 'always' by 'accessible'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
installation,
delete 'installation,' by ''
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
require
delete 'require' by ''
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
not
replace 'not' by 'and'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
do
replace 'do' by 'up-to-date,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
applications
replace 'applications' by 'always'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
web
replace 'web' by 'are'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
software,
replace 'software,' by 'installation,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
native
replace 'native' by 'require'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
classical
replace 'classical' by 'not'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
unlike
replace 'unlike' by 'do'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
benefits:
replace 'benefits:' by 'applications'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
highlighted
replace 'highlighted' by 'web'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
immediately
replace 'immediately' by 'software,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
content
replace 'content' by 'native'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
web
replace 'web' by 'classical'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
Interactive
replace 'Interactive' by 'unlike'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
client-side.
replace 'client-side.' by 'benefits:'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
the
replace 'the' by 'highlighted'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
on
replace 'on' by 'immediately'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
execution
replace 'execution' by 'content'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
code
replace 'code' by 'web'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
allowed
replace 'allowed' by 'Interactive'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
that
replace 'that' by 'client-side.'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
language
replace 'language' by 'the'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
programming
replace 'programming' by 'on'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
a
replace 'a' by 'execution'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
[3],
replace '[3],' by 'code'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
JavaScript
replace 'JavaScript' by 'allowed'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
introducing
replace 'introducing' by 'that'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
by
replace 'by' by 'language'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
concept
replace 'concept' by 'programming'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
this
replace 'this' by 'a'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
revolutionized
replace 'revolutionized' by '[3],'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
browser
replace 'browser' by 'JavaScript'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
Netscape
replace 'Netscape' by 'introducing'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
the
replace 'the' by 'by'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
1995,
replace '1995,' by 'concept'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
In
replace 'In' by 'this'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
pages.
replace 'pages.' by 'revolutionized'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
web
replace 'web' by 'browser'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
with
replace 'with' by 'Netscape'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
conjunction
replace 'conjunction' by 'the'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
in
replace 'in' by '1995,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
code
replace 'code' by 'In'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
executing
replace 'executing' by 'pages.'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
of
replace 'of' by 'web'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
concept
replace 'concept' by 'with'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
the
replace 'the' by 'conjunction'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
to
replace 'to' by 'in'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
rise
replace 'rise' by 'code'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
gave
replace 'gave' by 'executing'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
experiences
replace 'experiences' by 'of'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
user
replace 'user' by 'concept'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
engaging
replace 'engaging' by 'the'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
more
replace 'more' by 'to'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
for
replace 'for' by 'rise'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
demand
replace 'demand' by 'gave'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
the
replace 'the' by 'experiences'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
and
replace 'and' by 'user'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
companies,
replace 'companies,' by 'engaging'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
internet-based
replace 'internet-based' by 'more'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
of
replace 'of' by 'for'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
valuation
replace 'valuation' by 'demand'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
insert '' by 'and'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
internet,
replace 'internet,' by 'companies,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
the
replace 'the' by 'internet-based'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
proliferation
replace 'proliferation' by 'valuation'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
insert '' by 'internet,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
devices,
replace 'devices,' by 'the'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
insert '' by 'proliferation'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
insert '' by 'the'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
insert '' by 'devices,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
insert '' by 'of'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
inception,
replace 'inception,' by 'Inception,'
Inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT's first web browser, Nexus, made its appearance in 1990 [2]. At its best.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
inception
This sentence does not start with an uppercase letter.
inception, web browsing consisted solely of retrieving and displaying small, static text pages. With Nexus, users could access for the first time interlinked hypertext documents, so-called HTML pages. However, the escalating computing power of devices, the proliferation of the internet, the valuation of internet-based companies, and the demand for more engaging user experiences gave rise to the concept of executing code in conjunction with web pages. In 1995, the Netscape browser revolutionized this concept by introducing JavaScript [3], a programming language that allowed code execution on the client-side. Interactive web content immediately highlighted benefits: unlike classical native software, web applications do not require installation, are always up-to-date, and are accessible from any device with a web browser. Significantly, since the advent of Netscape, all browsers offer JavaScript support. In the present day, the majority of web pages incorporate not only HTML but also JavaScript code, which is executed on client computers. Consequently, over the past several decades, web browsers have evolved into intricate systems capable of running comprehensive applications, such as video and audio players, animation creators, and PDF document renderers. TT first web browser, Nexus, made its appearance in 1990 [2]. At its
side?.
replace 'side?.' by 'side?'
Despite being the main scripting language in modern web browsers, JavaScript has limitations due to its unique language characteristics [4]. Each JavaScript engine requires the parsing and recompiling of the JavaScript code, thereby causing substantial overhead. In practice, the process of parsing and compiling JavaScript code constitutes the majority of website load times. Additionally, JavaScript presents security issues, including the lack of memory isolation, which potentially enables information extraction from other processes [5, 6]. Numerous attempts have been made to port other languages, offering different guarantees, to the browser execution as alternatives to JavaScript. For instance, Java applets emerged on web pages in the late 90s, enabling the execution of Java bytecode on the client side? Likewise, Microsoft proposed ActiveX in 1996°, and Silverlight in 2007*. However, these attempts either failed to persist or experienced low adoption, primarily due to security issues and the absence of consensus among the community of browser vendors.
Despite being the main scripting language in modern web browsers, JavaScript has limitations due to its unique language characteristics [4]. Each JavaScript engine requires the parsing and recompiling of the JavaScript code, thereby causing substantial overhead. In practice, the process of parsing and compiling JavaScript code constitutes the majority of website load times. Additionally, JavaScript presents security issues, including the lack of memory isolation, which potentially enables information extraction from other processes [5, 6]. Numerous attempts have been made to port other languages, offering different guarantees, to the browser execution as alternatives to JavaScript. For instance, Java applets emerged on web pages in the late 90s, enabling the execution of Java bytecode on the client side?. Likewise, Microsoft proposed ActiveX in 1996°, and Silverlight in 2007*. However, these attempts either failed to persist or experienced low adoption, primarily due to security issues and the absence of consensus among the community of browser vendors.
“nttps://www.microsoft.com/silverlight/
replace '“nttps://www.microsoft.com/silverlight/' by '“nttps://www.microsoft.com/silverlight/.'
lhttps://hacks.mozilla.org/2017/02/what-makes-webassembly-fast *https://www.oracle.com/java/technologies/javase/9-deprecated-features. html 3https://web. Archive. org/web/20090828024117/http://www.microsoft.com/presspass/pr ess/1996/mar96/activxpr.mspx “nttps://www.microsoft.com/silverlight/.
lhttps://hacks.mozilla.org/2017/02/what-makes-webassembly-fast *https://www.oracle.com/java/technologies/javase/9-deprecated-features. html 3https://web. archive. org/web/20090828024117/http://www.microsoft.com/presspass/pr ess/1996/mar96/activxpr.mspx “nttps://www.microsoft.com/silverlight/
archive.
replace 'archive.' by 'Archive.'
lhttps://hacks.mozilla.org/2017/02/what-makes-webassembly-fast *https://www.oracle.com/java/technologies/javase/9-deprecated-features. html 3https://web. Archive. org/web/20090828024117/http://www.microsoft.com/presspass/pr ess/1996/mar96/activxpr.mspx “nttps://www.microsoft.com/silverlight/.
lhttps://hacks.mozilla.org/2017/02/what-makes-webassembly-fast *https://www.oracle.com/java/technologies/javase/9-deprecated-features. html 3https://web. archive. org/web/20090828024117/http://www.microsoft.com/presspass/pr ess/1996/mar96/activxpr.mspx “nttps://www.microsoft.com/silverlight/
lhttps
This sentence does not start with an uppercase letter.
lhttps://hacks.mozilla.org/2017/02/what-makes-webassembly-fast *https://www.oracle.com/java/technologies/javase/9-deprecated-features. html 3https://web. archive. org/web/20090828024117/http://www.microsoft.com/presspass/pr ess/1996/mar96/activxpr.mspx “nttps://www.microsoft.com/silverlight/
WebAssembly?.
replace 'WebAssembly?.' by 'WebAssembly?'
Importantly, WebAssembly provides a platform for compiling several legacy code applications, like those written in C/C++. For example, LLVM includes WebAssembly as a backend since release 7.1.0 published in May 2019%. WebAssembly paves the way for web applications to undertake roles traditionally reserved for native desktop applications, having the potential to transform web software as we know it. For example, applications such as AutoCAD and Adobe Photoshop have been ported to WebAssembly?
Importantly, WebAssembly provides a platform for compiling several legacy code applications, like those written in C/C++. For example, LLVM includes WebAssembly as a backend since release 7.1.0 published in May 2019%. WebAssembly paves the way for web applications to undertake roles traditionally reserved for native desktop applications, having the potential to transform web software as we know it. For example, applications such as AutoCAD and Adobe Photoshop have been ported to WebAssembly?.
to
delete 'to' by ''
The first distinction from earlier attempts to port non-JavaScript languages to the web lies in WebAssembly’s initial design. Unlike its predecessors, WebAssembly was crafted to supplement JavaScript in the browser as a platform-agnostic, low-level bytecode, rather than completely replace it. Its primary goal was to replace computing-intensive JavaScript code in contemporary web applications. Additionally, WebAssembly is the inaugural major language that used formal specification and verification right from the design inception [7, 8].
The first distinction from earlier attempts to port non-JavaScript languages to the web lies in WebAssembly’s initial design. Unlike its predecessors, WebAssembly was crafted to supplement JavaScript in the browser as a platform-agnostic, low-level bytecode, rather than to completely replace it. Its primary goal was to replace computing-intensive JavaScript code in contemporary web applications. Additionally, WebAssembly is the inaugural major language that used formal specification and verification right from the design inception [7, 8].
the
delete 'the' by ''
Importantly, in 2014, Alon Zakai et al. The Emscripten tool®. Emscripten employs a strict subset of JavaScript, asm.js, to facilitate the compilation of low-level code such as C to JavaScript. Asm.js was included as an LLVM backend®. This strategy offered the advantages of the ahead-of-time optimizations from LLVM, resulting in performance gains on browser clients’ when compared to standard JavaScript code. Asm.js outperformed JavaScript because it restricted language features to those that could be optimized in the LLVM pipeline. Moreover, it eliminated most of the language’s dynamic characteristics, limiting it to numerical types, top-level functions, and one large array in memory accessed directly as raw data. Asm.js proved that client-side code could be enhanced with the appropriate language design and standardization. In response to persistent JavaScript-related issues, the formalization and creation of a formal specification following asm.js laid the groundwork for the emergence of WebAssembly as a fast, low-level, portable bytecode for browsers. In 2015, the Web Consortium (W3C) standardized WebAssembly. As a result, WebAssembly bytecode became the fourth official language for the web.
Importantly, in 2014, Alon Zakai et al. proposed the Emscripten tool®. Emscripten employs a strict subset of JavaScript, asm.js, to facilitate the compilation of low-level code such as C to JavaScript. Asm.js was included as an LLVM backend®. This strategy offered the advantages of the ahead-of-time optimizations from LLVM, resulting in performance gains on browser clients’ when compared to standard JavaScript code. Asm.js outperformed JavaScript because it restricted language features to those that could be optimized in the LLVM pipeline. Moreover, it eliminated most of the language’s dynamic characteristics, limiting it to numerical types, top-level functions, and one large array in memory accessed directly as raw data. Asm.js proved that client-side code could be enhanced with the appropriate language design and standardization. In response to persistent JavaScript-related issues, the formalization and creation of a formal specification following asm.js laid the groundwork for the emergence of WebAssembly as a fast, low-level, portable bytecode for browsers. In 2015, the Web Consortium (W3C) standardized WebAssembly. As a result, WebAssembly bytecode became the fourth official language for the web.
proposed
replace 'proposed' by 'The'
Importantly, in 2014, Alon Zakai et al. The Emscripten tool®. Emscripten employs a strict subset of JavaScript, asm.js, to facilitate the compilation of low-level code such as C to JavaScript. Asm.js was included as an LLVM backend®. This strategy offered the advantages of the ahead-of-time optimizations from LLVM, resulting in performance gains on browser clients’ when compared to standard JavaScript code. Asm.js outperformed JavaScript because it restricted language features to those that could be optimized in the LLVM pipeline. Moreover, it eliminated most of the language’s dynamic characteristics, limiting it to numerical types, top-level functions, and one large array in memory accessed directly as raw data. Asm.js proved that client-side code could be enhanced with the appropriate language design and standardization. In response to persistent JavaScript-related issues, the formalization and creation of a formal specification following asm.js laid the groundwork for the emergence of WebAssembly as a fast, low-level, portable bytecode for browsers. In 2015, the Web Consortium (W3C) standardized WebAssembly. As a result, WebAssembly bytecode became the fourth official language for the web.
Importantly, in 2014, Alon Zakai et al. proposed the Emscripten tool®. Emscripten employs a strict subset of JavaScript, asm.js, to facilitate the compilation of low-level code such as C to JavaScript. Asm.js was included as an LLVM backend®. This strategy offered the advantages of the ahead-of-time optimizations from LLVM, resulting in performance gains on browser clients’ when compared to standard JavaScript code. Asm.js outperformed JavaScript because it restricted language features to those that could be optimized in the LLVM pipeline. Moreover, it eliminated most of the language’s dynamic characteristics, limiting it to numerical types, top-level functions, and one large array in memory accessed directly as raw data. Asm.js proved that client-side code could be enhanced with the appropriate language design and standardization. In response to persistent JavaScript-related issues, the formalization and creation of a formal specification following asm.js laid the groundwork for the emergence of WebAssembly as a fast, low-level, portable bytecode for browsers. In 2015, the Web Consortium (W3C) standardized WebAssembly. As a result, WebAssembly bytecode became the fourth official language for the web.
9lw
delete '9lw' by ''
“https://emscripten.org/ Shttp://asmjs.org/spec/latest/ “https://hacks.mozilla.org/2015/03/asm-speedups-everywhere/ Shttps://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 Snttps://twitter.com/Adobe/status/14530348050046853137
“https://emscripten.org/ Shttp://asmjs.org/spec/latest/ “https://hacks.mozilla.org/2015/03/asm-speedups-everywhere/ Shttps://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 Snttps://twitter.com/Adobe/status/14530348050046853137s=20&t=Zf 1N7-WmzecAOK4V8R6 9lw
1N7-WmzecAOK4V8R6
delete '1N7-WmzecAOK4V8R6' by ''
“https://emscripten.org/ Shttp://asmjs.org/spec/latest/ “https://hacks.mozilla.org/2015/03/asm-speedups-everywhere/ Shttps://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 Snttps://twitter.com/Adobe/status/14530348050046853137
“https://emscripten.org/ Shttp://asmjs.org/spec/latest/ “https://hacks.mozilla.org/2015/03/asm-speedups-everywhere/ Shttps://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 Snttps://twitter.com/Adobe/status/14530348050046853137s=20&t=Zf 1N7-WmzecAOK4V8R6 9lw
Snttps://twitter.com/Adobe/status/14530348050046853137s=20&t=Zf
replace 'Snttps://twitter.com/Adobe/status/14530348050046853137s=20&t=Zf' by 'Snttps://twitter.com/Adobe/status/14530348050046853137'
“https://emscripten.org/ Shttp://asmjs.org/spec/latest/ “https://hacks.mozilla.org/2015/03/asm-speedups-everywhere/ Shttps://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 Snttps://twitter.com/Adobe/status/14530348050046853137
“https://emscripten.org/ Shttp://asmjs.org/spec/latest/ “https://hacks.mozilla.org/2015/03/asm-speedups-everywhere/ Shttps://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 Snttps://twitter.com/Adobe/status/14530348050046853137s=20&t=Zf 1N7-WmzecAOK4V8R6 9lw
and,
replace 'and,' by 'and'
The extensive applicability and rapid adoption of WebAssembly have prompted requests for additional features. However, these demands do not always align with the initial specifications. For extending WebAssembly with a new proposal, it must satisfy particular criteria. A new proposal needs a formal specification and at least two implementations, e.g., two different WebAssembly engines. This approach allows for swift incorporation of new formalization and features via the so-called “evergreen method” while maintaining the original WebAssembly specification intact. Since the inception of WebAssembly, numerous extensions have been proposed for standardization. For instance, the SIMD proposal enables the execution of vectorized instructions in WebAssembly. After approval, new extensions remain optional, ensuring that the core WebAssembly version remains 1.0. The ongoing development of WebAssembly provides avenues for research and development. However, it also gives rise to security concerns within the ecosystem, as new threats emerge.
The extensive applicability and rapid adoption of WebAssembly have prompted requests for additional features. However, these demands do not always align with the initial specifications. For extending WebAssembly with a new proposal, it must satisfy particular criteria. A new proposal needs a formal specification and, at least two implementations, e.g., two different WebAssembly engines. This approach allows for swift incorporation of new formalization and features via the so-called “evergreen method” while maintaining the original WebAssembly specification intact. Since the inception of WebAssembly, numerous extensions have been proposed for standardization. For instance, the SIMD proposal enables the execution of vectorized instructions in WebAssembly. After approval, new extensions remain optional, ensuring that the core WebAssembly version remains 1.0. The ongoing development of WebAssembly provides avenues for research and development. However, it also gives rise to security concerns within the ecosystem, as new threats emerge.
self-containment.
replace 'self-containment.' by 'self-containedness.'
The WebAssembly specification embodies several language design principles that pave the way for its extension beyond the web ecosystem. For instance, the architecture of WebAssembly guarantees self-containedness. Inherently, WebAssembly binaries are prohibited from accessing memory beyond their own designated space, thereby amplifying security via Software Fault Isolation (SFI) policies [9]. Consequently, research has highlighted the benefits of integrating WebAssembly as an intermediate layer in contemporary cloud platforms [10]. In particular, the employment of WebAssembly binaries improves startup times and optimizes memory consumption, outperforming virtualization and containerization [11]. Furthermore, compared to virtual machines and containers, WebAssembly programs are more compact, highlighting their efficient deployment, especially when network transportation is a consideration. The methodology for standalone WebAssembly execution was formalized in 2019 when the Bytecode Alliance proposed the WebAssembly System Interface (WASI)?°. WASI standardizes the execution of WebAssembly via a POSIX-like interface protocol, thereby facilitating the execution of WebAssembly closer to operating systems. This standardization enables WebAssembly to function outside web browsers, extending its use to cloud environments and IoT devices [12, 13].
The WebAssembly specification embodies several language design principles that pave the way for its extension beyond the web ecosystem. For instance, the architecture of WebAssembly guarantees self-containment. Inherently, WebAssembly binaries are prohibited from accessing memory beyond their own designated space, thereby amplifying security via Software Fault Isolation (SFI) policies [9]. Consequently, research has highlighted the benefits of integrating WebAssembly as an intermediate layer in contemporary cloud platforms [10]. In particular, the employment of WebAssembly binaries improves startup times and optimizes memory consumption, outperforming virtualization and containerization [11]. Furthermore, compared to virtual machines and containers, WebAssembly programs are more compact, highlighting their efficient deployment, especially when network transportation is a consideration. The methodology for standalone WebAssembly execution was formalized in 2019 when the Bytecode Alliance proposed the WebAssembly System Interface (WASI)?°. WASI standardizes the execution of WebAssembly via a POSIX-like interface protocol, thereby facilitating the execution of WebAssembly closer to operating systems. This standardization enables WebAssembly to function outside web browsers, extending its use to cloud environments and IoT devices [12, 13].
//github.com/WebAssembly/WASI
replace '//github.com/WebAssembly/WASI' by '//github.com/WebAssembly/WASI.'
l0ntps: //github.com/WebAssembly/WASI.
l0nttps: //github.com/WebAssembly/WASI
l0nttps:
replace 'l0nttps:' by 'l0ntps:'
l0ntps: //github.com/WebAssembly/WASI.
l0nttps: //github.com/WebAssembly/WASI
l0nttps
Possible typo detected.
l0nttps: //github.com/WebAssembly/WASI
attack!!.
replace 'attack!!.' by 'attack!!'
The above issue is exacerbated when considering the adoption of WebAssembly by edge-cloud computing platforms to provide services. In addition to browser clients, thousands of edge devices operate millions of identical WebAssembly instantiations per second [17]. This suggests that a single vulnerable WebAssembly binary in an edge network node could render every node identically susceptible due to the binary replication occurring on each node. A potential attacker could compromise all edge nodes concurrently, implying that a single distributed WebAssembly binary could trigger a global attack!!
The aforementioned issue is exacerbated when considering the adoption of WebAssembly by edge-cloud computing platforms to provide services. In addition to browser clients, thousands of edge devices operate millions of identical WebAssembly instantiations per second [17]. This suggests that a single vulnerable WebAssembly binary in an edge network node could render every node identically susceptible due to the binary replication occurring on each node. A potential attacker could compromise all edge nodes concurrently, implying that a single distributed WebAssembly binary could trigger a global attack!!.
aforementioned
replace 'aforementioned' by 'above'
The above issue is exacerbated when considering the adoption of WebAssembly by edge-cloud computing platforms to provide services. In addition to browser clients, thousands of edge devices operate millions of identical WebAssembly instantiations per second [17]. This suggests that a single vulnerable WebAssembly binary in an edge network node could render every node identically susceptible due to the binary replication occurring on each node. A potential attacker could compromise all edge nodes concurrently, implying that a single distributed WebAssembly binary could trigger a global attack!!
The aforementioned issue is exacerbated when considering the adoption of WebAssembly by edge-cloud computing platforms to provide services. In addition to browser clients, thousands of edge devices operate millions of identical WebAssembly instantiations per second [17]. This suggests that a single vulnerable WebAssembly binary in an edge network node could render every node identically susceptible due to the binary replication occurring on each node. A potential attacker could compromise all edge nodes concurrently, implying that a single distributed WebAssembly binary could trigger a global attack!!.
ecosystems
replace 'ecosystems' by 'ecosystems.'
Predictability in WebAssembly ecosystems.
Predictability in WebAssembly ecosystems
l1nttps
Possible typo detected.
l1nttps://www.fastly.com/blog/defense-in-depth-stopping-a-wasm-compiler-bug-befo re-it-became-a-problem
the
delete 'the' by ''
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
demonstrated
delete 'demonstrated' by ''
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
al.
replace 'al.' by 'The'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
et
replace 'et' by 'al.'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
Genkin
replace 'Genkin' by 'et'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
deployments,
replace 'deployments,' by 'Genkin'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
standalone
replace 'standalone' by 'deployments,'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
In
replace 'In' by 'standalone'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
[21].
replace '[21].' by 'In'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
executions
replace 'executions' by '[21].'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
during
replace 'during' by 'WebAssembly'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
crashes
replace 'crashes' by 'during'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
and
replace 'and' by 'crashes'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
overflows
replace 'overflows' by 'and'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
unnoticed
replace 'unnoticed' by 'overflows'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
in
replace 'in' by 'unnoticed'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
result
replace 'result' by 'in'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
could
replace 'could' by 'result'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
protections
replace 'protections' by 'could'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
stack-smashing
replace 'stack-smashing' by 'protections'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
of
replace 'of' by 'stack-smashing'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
lack
replace 'lack' by 'of'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
the
replace 'the' by 'lack'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
Besides,
replace 'Besides,' by 'the'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
code.
replace 'code.' by 'Besides,'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
source
replace 'source' by 'code.'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
the
replace 'the' by 'source'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
in
replace 'in' by 'the'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
flaws
replace 'flaws' by 'in'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
to
replace 'to' by 'flaws'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
due
replace 'due' by 'to'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
binaries
replace 'binaries' by 'due'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
in
replace 'in' by 'WebAssembly'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
exist
replace 'exist' by 'in'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
can
replace 'can' by 'exist'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
vulnerabilities
replace 'vulnerabilities' by 'can'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
innate
replace 'innate' by 'vulnerabilities'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
as
replace 'as' by 'innate'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
20]
replace '20]' by 'as'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
[19,
replace '[19,' by '20]'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
feasible
replace 'feasible' by '[19,'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
are
replace 'are' by 'feasible'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
itself
replace 'itself' by 'are'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
of
replace 'of' by 'WebAssembly'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
memory
replace 'memory' by 'of'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
the
replace 'the' by 'memory'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
within
replace 'within' by 'the'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
attacks
replace 'attacks' by 'within'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
Moreover,
replace 'Moreover,' by 'attacks'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
feasible.
replace 'feasible.' by 'Moreover,'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
always
replace 'always' by 'feasible.'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
not
replace 'not' by 'always'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
are
replace 'are' by 'not'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
which
replace 'which' by 'are'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
changes,
replace 'changes,' by 'which'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
operating-level
replace 'operating-level' by 'changes,'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
and
replace 'and' by 'operating-level'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
hardware
replace 'hardware' by 'and'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
involve
replace 'involve' by 'hardware'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
often
replace 'often' by 'involve'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
mitigations
replace 'mitigations' by 'often'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
cases,
replace 'cases,' by 'mitigations'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
such
replace 'such' by 'cases,'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
In
replace 'In' by 'such'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
[18].
replace '[18].' by 'In'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
browsers
replace 'browsers' by '[18].'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
in
replace 'in' by 'browsers'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
malware
replace 'malware' by 'in'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
using
replace 'using' by 'WebAssembly'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
attacks
replace 'attacks' by 'using'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
side-channel
replace 'side-channel' by 'attacks'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
contention
replace 'contention' by 'side-channel'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
port
replace 'port' by 'contention'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
of
replace 'of' by 'port'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
risk
replace 'risk' by 'of'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
potential
replace 'potential' by 'risk'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
the
replace 'the' by 'potential'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
highlighted
replace 'highlighted' by 'The'
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. The potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. The possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
We devise two scenarios where predictability affects WebAssembly ecosystems. First, the predictability of execution engines and WebAssembly binaries themselves facilitates side-channel and memory attacks. Despite the praise for WebAssembly’s security, particularly its design that prohibits programs from accessing data beyond their own memory, it is not immune to such vulnerabilities. For example, Rokicki et al. highlighted the potential risk of port contention side-channel attacks using WebAssembly malware in browsers [18]. In such cases, mitigations often involve hardware and operating-level changes, which are not always feasible. Moreover, attacks within the memory of WebAssembly itself are feasible [19, 20] as innate vulnerabilities can exist in WebAssembly binaries due to flaws in the source code. Besides, the lack of stack-smashing protections could result in unnoticed overflows and crashes during WebAssembly executions [21]. In standalone deployments, Genkin et al. demonstrated the possibility of data extraction via cache-timing side channels in WebAssembly [22]. In a similar vein, Maisuradze and Rossow exhibited speculative execution attacks on WebAssembly binaries [23].
Diversification
replace 'Diversification' by 'Diversification.'
Approach: Software Diversification.
Approach: Software Diversification
pose
replace 'pose' by 'poses'
To sum up, predictability and potential vulnerabilities form a harmful combination. This principle does not exclude WebAssembly and its ecosystem. The effect of exploiting a single vulnerability in WebAssembly could prove catastrophic, given that all devices running the same WebAssembly binaries could be affected. On the other hand, WebAssembly malware poses a severe threat. Present defenses may not adequately protect against them, as they have not been designed to manage situations outside predictable scenarios, such as obfuscation. Besides, mitigations might require hardware and operating-level changes, which are not always feasible. In this dissertation, we tackle the subsequent two problems:
To sum up, predictability and potential vulnerabilities form a harmful combination. This principle does not exclude WebAssembly and its ecosystem. The effect of exploiting a single vulnerability in WebAssembly could prove catastrophic, given all devices running the same WebAssembly binaries could be affected. On the other hand, WebAssembly malware pose a severe threat. Present defenses may not adequately protect against them, as they have not been designed to manage situations outside predictable scenarios, such as obfuscation. Besides, mitigations might require hardware and operating-level changes, which are not always feasible. In this dissertation, we tackle the subsequent two problems:
insert '' by 'that'
To sum up, predictability and potential vulnerabilities form a harmful combination. This principle does not exclude WebAssembly and its ecosystem. The effect of exploiting a single vulnerability in WebAssembly could prove catastrophic, given that all devices running the same WebAssembly binaries could be affected. On the other hand, WebAssembly malware poses a severe threat. Present defenses may not adequately protect against them, as they have not been designed to manage situations outside predictable scenarios, such as obfuscation. Besides, mitigations might require hardware and operating-level changes, which are not always feasible. In this dissertation, we tackle the subsequent two problems:
To sum up, predictability and potential vulnerabilities form a harmful combination. This principle does not exclude WebAssembly and its ecosystem. The effect of exploiting a single vulnerability in WebAssembly could prove catastrophic, given all devices running the same WebAssembly binaries could be affected. On the other hand, WebAssembly malware pose a severe threat. Present defenses may not adequately protect against them, as they have not been designed to manage situations outside predictable scenarios, such as obfuscation. Besides, mitigations might require hardware and operating-level changes, which are not always feasible. In this dissertation, we tackle the subsequent two problems:
statements
replace 'statements' by 'Statements.'
Problem Statements.
Problem statements
enunciated
replace 'enunciated' by 'enumerated'
This dissertation introduces tools, strategies, and methodologies designed to address the previously enumerated problems via Software Diversification. Software Diversification is a security strategy that involves identifying, developing, and deploying program variants of a given original program [32]. Pioneers in this field, Cohen et al. [33] and Forrest et al. [34], proposed enhancing software diversity through code transformations. Their proposal recommended the creation of diverse program variants, maintaining their original functionalities. Software Diversification aims to lessen potential vulnerabilities by enhancing behavior unpredictability in observabilities used to conduct attacks, e.g., side-channels.
This dissertation introduces tools, strategies, and methodologies designed to address the previously enunciated problems via Software Diversification. Software Diversification is a security strategy that involves identifying, developing, and deploying program variants of a given original program [32]. Pioneers in this field, Cohen et al. [33] and Forrest et al. [34], proposed enhancing software diversity through code transformations. Their proposal recommended the creation of diverse program variants, maintaining their original functionalities. Software Diversification aims to lessen potential vulnerabilities by enhancing behavior unpredictability in observabilities used to conduct attacks, e.g., side-channels.
usage
delete 'usage' by ''
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
practical
delete 'practical' by ''
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
the
delete 'the' by ''
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
about
delete 'about' by ''
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
insights
replace 'insights' by 'use'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
empirical
replace 'empirical' by 'practical'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
provide
replace 'provide' by 'the'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
We
replace 'We' by 'about'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
purposes.
replace 'purposes.' by 'insights'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
defensive
replace 'defensive' by 'empirical'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
for
replace 'for' by 'provide'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
used
replace 'used' by 'We'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
be
replace 'be' by 'purposes.'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
could
replace 'could' by 'defensive'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
variants
replace 'variants' by 'for'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
program
replace 'program' by 'used'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
diversified
replace 'diversified' by 'could'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
how
replace 'how' by 'variants'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
assess
replace 'assess' by 'program'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
we
replace 'we' by 'WebAssembly'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
P1,
replace 'P1,' by 'diversified'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
address
replace 'address' by 'how'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
to
replace 'to' by 'assess'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
order
replace 'order' by 'we'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
In
replace 'In' by 'P1,'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
Technique:
replace 'Technique:' by 'address'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
Diversification
replace 'Diversification' by 'to'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
Defensive
replace 'Defensive' by 'order'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
C2
replace 'C2' by 'In'
In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical use of the generated variants in preventing attacks.
C2 Defensive Diversification Technique: In order to address P1, we assess how diversified WebAssembly program variants could be used for defensive purposes. We provide empirical insights about the practical usage of the generated variants in preventing attacks.
Technique:
delete 'Technique:' by ''
In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.
C1 Offensive Diversification Technique: In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.
Diversification
delete 'Diversification' by ''
In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.
C1 Offensive Diversification Technique: In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.
Offensive
delete 'Offensive' by ''
In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.
C1 Offensive Diversification Technique: In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.
C1
delete 'C1' by ''
In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.
C1 Offensive Diversification Technique: In order to address P2, we evaluate the potential for using generated WebAssembly program variants for offensive purposes. Our research includes experiments where we test the resilience of WebAssembly analysis tools against these generated variants. Furthermore, we offer insights into which types of program variants practitioners should prioritize to improve WebAssembly analysis tools.
insert '' by '[35].'
Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
1989.
replace '1989.' by 'diversification'
Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
in
replace 'in' by 'of'
Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
early
replace 'early' by 'benefits'
Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
[35]
replace '[35]' by 'practical'
Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
diversification
replace 'diversification' by 'the'
Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
of
replace 'of' by 'underscored'
Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
benefits
replace 'benefits' by 'he'
Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
practical
replace 'practical' by '1989,'
Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
the
replace 'the' by 'in'
Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
underscored
replace 'underscored' by 'Early'
Eichin et al. Early in 1989, he underscored the practical benefits of diversification [35]. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
Eichin et al. underscored the practical benefits of diversification [35] early in 1989. They illustrated how diversification limited the exploitation of the Morris Worm to a few machines. From an attacker’s perspective, the diversity of target systems rendered them unpredictable. Therefore, Software Diversification effectively removes vulnerabilities. For WebAssembly, Software Diversification could bolster browsers and standalone engines by providing diversified program variants, making it harder for attackers to exploit vulnerabilities, addressing Pl. Furthermore, it could increase the accuracy of WebAssembly malware detectors and WebAssembly analysis tools in general, addressing P2. However, the implementation of Software Diversification in WebAssembly is still largely unexplored. In light of this, we offer the following contributions within the context of Software Diversification, which are not necessarily mutually exclusive.
~
delete '~' by ''
Contribution Research papers I Wt m tv [36] [37] [38] [39] C1 Offensive diversification technique w C2 Defensive diversification technique | WY WW C3 Extensive experimental evaluation | WY WY WY WW
Contribution Research papers I Wt m tv [36] [37] [38] [39] C1 Offensive diversification technique w~ C2 Defensive diversification technique | ~ WY WW C3 Extensive experimental evaluation | WY WY WY WW
w~
replace 'w~' by 'w'
Contribution Research papers I Wt m tv [36] [37] [38] [39] C1 Offensive diversification technique w C2 Defensive diversification technique | WY WW C3 Extensive experimental evaluation | WY WY WY WW
Contribution Research papers I Wt m tv [36] [37] [38] [39] C1 Offensive diversification technique w~ C2 Defensive diversification technique | ~ WY WW C3 Extensive experimental evaluation | WY WY WY WW
technique
delete 'technique' by ''
For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
proposed
delete 'proposed' by ''
For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
each
delete 'each' by ''
For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
For
delete 'For' by ''
For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
evaluation:
replace 'evaluation:' by 'technique,'
For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
experimental
replace 'experimental' by 'proposed'
For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
Extensive
replace 'Extensive' by 'each'
For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
C3
replace 'C3' by 'For'
For each proposed technique, we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
C3 Extensive experimental evaluation: For each proposed technique we provide an artifact implementation and conduct experiments to assess its capabilities. The artifacts are publicly available. The protocols and results of assessing the artifacts provide guidance for future research on P1 and P2.
insert '' by '3564007)'
II: Multivariant execution at the Edge. Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007)
II: Multivariant execution at the Edge. Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007
insert '' by '-'
II: Multivariant execution at the Edge. Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007)
II: Multivariant execution at the Edge. Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007
3564007
replace '3564007' by '3564007)'
II: Multivariant execution at the Edge. Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007) - 3564007)
II: Multivariant execution at the Edge. Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus, Benoit Baudry Workshop on Moving Target Defense (MTD 2022), 12 pages https://dl.acm.org/doi/abs/10.1145/3560828. 3564007
Summary:
replace 'Summary:' by 'Summary'
Summary In this paper, we introduce the first entirely automated workflow for diversifying WebAssembly binaries. We present CROW, an open-source tool that implements Software Diversification through enumerative synthesis. We assess the capabilities of CROW and examine its application on real-world, security-sensitive programs. In general, CROW can create thousands of statically diverse variants. Furthermore, we illustrate that the generated variants exhibit different behaviors at runtime.
Summary: In this paper, we introduce the first entirely automated workflow for diversifying WebAssembly binaries. We present CROW, an open-source tool that implements Software Diversification through enumerative synthesis. We assess the capabilities of CROW and examine its application on real-world, security-sensitive programs. In general, CROW can create thousands of statically diverse variants. Furthermore, we illustrate that the generated variants exhibit different behaviors at runtime.
insert '' by '230'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
2021.
replace '2021.' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
https://doi.org/10.14722/madweb.
replace 'https://doi.org/10.14722/madweb.' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
pages
replace 'pages' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
12
replace '12' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
2021),
replace '2021),' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
(MADWeb
replace '(MADWeb' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Web
replace 'Web' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
the
replace 'the' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
for
replace 'for' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Defenses
replace 'Defenses' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
and
replace 'and' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Attacks,
replace 'Attacks,' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Measurements,
replace 'Measurements,' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Measurements
replace 'Measurements' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
on
replace 'on' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Workshop
replace 'Workshop' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Martin
replace 'Martin' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Baudry,
replace 'Baudry,' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Benoit
replace 'Benoit' by '23004'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Vera-Pérez,
replace 'Vera-Pérez,' by '2021.'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Oscar
replace 'Oscar' by 'https://doi.org/10.14722/madweb.'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Floros,
replace 'Floros,' by 'pages'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Orestis
replace 'Orestis' by '12'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Cabrera-Arteaga,
replace 'Cabrera-Arteaga,' by '2021),'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
Javier
replace 'Javier' by '(MADWeb'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
bytecode.
replace 'bytecode.' by 'Web'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Defenses'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'and'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
insert '' by 'Attacks,'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'Measurements,'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'Measurements'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'on'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'Workshop'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'Monperrus'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'Martin'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'Baudry,'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'Benoit'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'Vera-Pérez,'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'Oscar'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'Floros,'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'Orestis'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'Cabrera-Arteaga,'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'Javier'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'bytecode.'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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insert '' by 'WebAssembly'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
randomization
replace 'randomization' by 'for'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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Code
replace 'Code' by 'randomization'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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CROW:
replace 'CROW:' by 'Code'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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I:
replace 'I:' by 'CROW:'
CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 23004 230
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Measurements Measurements
Possible typo: you repeated a word
I: CROW: Code randomization for WebAssembly bytecode. Javier Cabrera-Arteaga, Orestis Floros, Oscar Vera-Pérez, Benoit Baudry, Martin Monperrus Workshop on Measurements Measurements, Attacks, and Defenses for the Web (MADWeb 2021), 12 pages https://doi.org/10.14722/madweb. 2021. 23004
1.1
replace '1.1' by '1.1,'
This compilation thesis comprises the following research papers. In Table 1.1, we map the contributions to our research papers.
This compilation thesis comprises the following research papers. In Table 1.1 we map the contributions to our research papers.
papers
replace 'papers' by 'papers.'
Summary of research papers.
Summary of research papers
Summary:
delete 'Summary:' by ''
In this paper, we synthesize functionally equivalent variants of deployed edge services. Service variants are encapsulated into a single multivariant WebAssembly binary. A random variant is selected and executed each time a function is invoked. Execution of multivariant binaries occurs on the global edge platform provided by Fastly, as part of a research collaboration. We demonstrate that multivariant binaries present a diverse range of execution traces throughout the entire edge platform, distributed worldwide, effectively creating a moving target defense.
Summary: In this paper, we synthesize functionally equivalent variants of deployed edge services. Service variants are encapsulated into a single multivariant WebAssembly binary. A random variant is selected and executed each time a function is invoked. Execution of multivariant binaries occurs on the global edge platform provided by Fastly, as part of a research collaboration. We demonstrate that multivariant binaries present a diverse range of execution traces throughout the entire edge platform, distributed worldwide, effectively creating a moving target defense.
pages
replace 'pages' by 'pages.'
IV. WebAssembly Diversification for Malware evasion. Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages.
IV: WebAssembly Diversification for Malware evasion. Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages
IV:
replace 'IV:' by 'IV.'
IV. WebAssembly Diversification for Malware evasion. Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages.
IV: WebAssembly Diversification for Malware evasion. Javier Cabrera-Arteaga, Tim Toady, Martin Monperrus, Benoit Baudry Computers & Security, Volume 131, 2028, 17 pages
Summary:
delete 'Summary:' by ''
This paper introduces WASM-MUTATE, a compiler-agnostic WebAssembly diversification engine. The engine is designed to swiftly generate functionally equivalent yet behaviorally diverse WebAssembly variants by randomly traversing e-graphs. E-graphs are specific graph data structures for representing and applying rewriting rules. We show that WASM-MUTATE can generate tens of thousands of unique WebAssembly variants in minutes. Importantly, WASM-MUTATE can safeguard WebAssembly binaries from timing side-channel attacks, such as Spectre.
Summary: This paper introduces WASM-MUTATE, a compiler-agnostic WebAssembly diversification engine. The engine is designed to swiftly generate functionally equivalent yet behaviorally diverse WebAssembly variants by randomly traversing e-graphs. E-graphs are specific graph data structures for representing and applying rewriting rules. We show that WASM-MUTATE can generate tens of thousands of unique WebAssembly variants in minutes. Importantly, WASM-MUTATE can safeguard WebAssembly binaries from timing side-channel attacks, such as Spectre.
00324
replace '00324' by '00324.'
Ill: WASM-MUTATE: Fast and efficient Software Diversification for WebAssembly. Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324.
Ill: WASM-MUTATE: Fast and efficient Software Diversification for WebAssembly. Javier Cabrera-Arteaga, Nicholas Fitzgerald, Martin Monperrus, Benoit Baudry Computers & Security, 2024, 20 pages https://www.sciencedirect.com/science/article/pii/S01674048240 00324
insert '' by 'outline.'
Thesis outline outline.
Thesis outline
summarises
replace 'summarises' by 'summarizes'
This dissertation comprises two parts as a compilation thesis. Part one summarizes the research papers included within, which is partially rooted in the author’s licentiate thesis [40]. Chapter 2 offers a background on WebAssembly and the latest advancements in Software Diversification. Chapter 3 delves into our technical contributions. Chapter 4 exhibits two use cases applying our technical contributions. Chapter 5 concludes the thesis and outlines future research directions. The second part of this thesis incorporates all the papers discussed in part one.
This dissertation comprises two parts as a compilation thesis. Part one summarises the research papers included within, which is partially rooted in the author’s licentiate thesis [40]. Chapter 2 offers a background on WebAssembly and the latest advancements in Software Diversification. Chapter 3 delves into our technical contributions. Chapter 4 exhibits two use cases applying our technical contributions. Chapter 5 concludes the thesis and outlines future research directions. The second part of this thesis incorporates all the papers discussed in part one.
Chapter
Three successive sentences begin with the same word. Consider rewording the sentence or use a thesaurus to find a synonym.
This dissertation comprises two parts as a compilation thesis. Part one summarises the research papers included within, which is partially rooted in the author’s licentiate thesis [40]. Chapter 2 offers a background on WebAssembly and the latest advancements in Software Diversification. Chapter 3 delves into our technical contributions. Chapter 4 exhibits two use cases applying our technical contributions. Chapter 5 concludes the thesis and outlines future research directions. The second part of this thesis incorporates all the papers discussed in part one.
insert '' by 'WebAssembly.'
Software Diversification. In Section 2.1, we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. The chapter discusses the state-of-the-art in the areas of WebAssembly and WebAssembly.
Software Diversification. In Section 2.1 we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. T"s chapter discusses the state-of-the-art in the areas of WebAssembly and
T"s
replace 'T"s' by 'The'
Software Diversification. In Section 2.1, we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. The chapter discusses the state-of-the-art in the areas of WebAssembly and WebAssembly.
Software Diversification. In Section 2.1 we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. T"s chapter discusses the state-of-the-art in the areas of WebAssembly and
2.1
replace '2.1' by '2.1,'
Software Diversification. In Section 2.1, we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. The chapter discusses the state-of-the-art in the areas of WebAssembly and WebAssembly.
Software Diversification. In Section 2.1 we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. T"s chapter discusses the state-of-the-art in the areas of WebAssembly and
"
Unpaired symbol: ‘"’ seems to be missing
Software Diversification. In Section 2.1 we discuss WebAssembly, focusing on its design and security model. Besides, we discuss the current state-of-the-art of WebAssembly research. In Section 2.2 we discuss related works in the area of Software Diversification. Moreover, we delve into the open challenges regarding the diversification of WebAssembly programs. T"s chapter discusses the state-of-the-art in the areas of WebAssembly and
insert '' by 'Tolkien.'
— J.R.R. Tolkien's Tolkien.
— J.R.R. Tolkien
Tolkien
replace 'Tolkien' by 'Tolkien's'
— J.R.R. Tolkien's Tolkien.
— J.R.R. Tolkien
Otherwise
replace 'Otherwise' by 'Otherwise,'
You must have a map, no matter how rough. Otherwise, you wander all over the place.
You must have a map, no matter how rough. Otherwise you wander all over the place.
Otherwise
A comma may be missing after the conjunctive/linking adverb ‘Otherwise’.
You must have a map, no matter how rough. Otherwise you wander all over the place.
ART
replace 'ART' by 'ART.'
BACKGROUND AND STATE OF THE ART.
BACKGROUND AND STATE OF THE ART
lThe
replace 'lThe' by 'The'
The WAT text format is primarily designed for human readability and for low-level manual editing.
lThe WAT text format is primarily designed for human readability and for low-level manual editing.
the
delete 'the' by ''
There are several compilers that turn source code into WebAssembly binaries. For example, LLVM compiles to WebAssembly as a backend option since its 7.1.0 release in early 20197, supporting a diverse set of frontend languages like C/C++, Rust, Go, and AssemblyScript®. Significantly, a study by Hilbig [45] reveals that 70% of WebAssembly binaries are generated using LLVM-based compilers. The main advantage of using LLVM is that it provides a modular and state-of-the-art optimization infrastructure for WebAssembly binaries. Today, Emscripten* is the most frequently used tool for porting C/C++ code to the Web as a drop-in replacement for a standard compiler like gcc or clang. The main advantage of Emscripten is that it provides a complete toolchain for compiling C/C++ code to WebAssembly, including the automatic generation of external functions for interacting with a Web host environment. Recently, the Kotlin Multiplatform framework® has incorporated WebAssembly as a compilation target, enabling the compilation of Kotlin code to WebAssembly. Similarly, the Cheerp® project proposes a Java Virtual Machine(JVM) fully ported to WebAssembly, supporting Java applications and legacy applets in the browser.
There are several compilers that turn source code into WebAssembly binaries. For example, LLVM compiles to WebAssembly as a backend option since its 7.1.0 release in early 20197, supporting a diverse set of frontend languages like C/C++, Rust, Go, and AssemblyScript®. Significantly, a study by Hilbig [45] reveals that 70% of WebAssembly binaries are generated using LLVM-based compilers. The main advantage of using LLVM is that it provides a modular and state-of-the-art optimization infrastructure for WebAssembly binaries. Today, Emscripten* is the most frequently used tool for porting C/C++ code to the Web as a drop-in replacement for a standard compiler like gcc or clang. The main advantage of Emscripten is that it provides a complete toolchain for compiling C/C++ code to WebAssembly, including the automatic generation of the external functions for interacting with a Web host environment. Recently, the Kotlin Multiplatform framework® has incorporated WebAssembly as a compilation target, enabling the compilation of Kotlin code to WebAssembly. Similarly, the Cheerp® project proposes a Java Virtual Machine(JVM) fully ported to WebAssembly, supporting Java applications and legacy applets in the browser.
20197
Did you mean to type a 4-digit year?
There are several compilers that turn source code into WebAssembly binaries. For example, LLVM compiles to WebAssembly as a backend option since its 7.1.0 release in early 20197, supporting a diverse set of frontend languages like C/C++, Rust, Go, and AssemblyScript®. Significantly, a study by Hilbig [45] reveals that 70% of WebAssembly binaries are generated using LLVM-based compilers. The main advantage of using LLVM is that it provides a modular and state-of-the-art optimization infrastructure for WebAssembly binaries. Today, Emscripten* is the most frequently used tool for porting C/C++ code to the Web as a drop-in replacement for a standard compiler like gcc or clang. The main advantage of Emscripten is that it provides a complete toolchain for compiling C/C++ code to WebAssembly, including the automatic generation of the external functions for interacting with a Web host environment. Recently, the Kotlin Multiplatform framework® has incorporated WebAssembly as a compilation target, enabling the compilation of Kotlin code to WebAssembly. Similarly, the Cheerp® project proposes a Java Virtual Machine(JVM) fully ported to WebAssembly, supporting Java applications and legacy applets in the browser.
insert '' by 'Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.'
*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.
*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive
Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive
replace 'Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive' by 'Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html'
*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.
*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive
html
replace 'html' by 'Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html'
*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.
*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive
Shttps://kotlinlang.org/docs/wasm-overview.
replace 'Shttps://kotlinlang.org/docs/wasm-overview.' by 'Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html'
*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.
*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive
insert '' by 'Shttps://kotlinlang.org/docs/wasm-overview.'
*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.
*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive
insert '' by 'html'
*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive.
*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive
“
Unpaired symbol: ‘”’ seems to be missing
*https://github.com/11vm/1lvm-project/releases/tag/llvmorg-7.1.0 3A subset of the TypeScript language “nttps://emscripten.org/docs/tools_reference/emcc. html Shttps://kotlinlang.org/docs/wasm-overview. html Shttps://labs.leaningtech.com/blog/cheerpj-3-deep-dive
insert '' by ';'
Block ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ;
block ; label @2
insert '' by '@2'
Block ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ;
block ; label @2
insert '' by 'label'
Block ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ;
block ; label @2
block
replace 'block' by 'Block'
Block ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ; label @2 ;
block ; label @2
block
This sentence does not start with an uppercase letter.
block ; label @2
insert '' by 'loc'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
insert '' by 'in'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
insert '' by 'saved'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
insert '' by 'is'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
insert '' by 'which'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
insert '' by 'element'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
insert '' by 'last'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
insert '' by 'the'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
insert '' by 'Return'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
insert '' by ';'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
insert '' by '3'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
insert '' by 'end'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
insert '' by 'O'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
insert '' by 'locpl'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
local.get
replace 'local.get' by 'Local.get'
Local.get 0 return ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in locpl O end 3 ; Return the last element which is saved in loc
local.get 0 return ; Return the last element which is saved in locpl O end 3
local
This sentence does not start with an uppercase letter.
local.get 0 return ; Return the last element which is saved in locpl O end 3
132.store
delete '132.store' by ''
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
end
delete 'end' by ''
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
0
delete '0' by ''
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
¢)
delete '¢)' by ''
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
Celine
delete 'Celine' by ''
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
"...
delete '"...' by ''
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
"name"
delete '"name"' by ''
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
(@custom
replace '(@custom' by ')'
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
"
replace '"' by 'Celine'
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
"__wbg__"
replace '"__wbg__"' by '"...'
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
(import
replace '(import' by '"name"'
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
"
replace '"' by '(@custom'
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
"__wbg__"
replace '"__wbg__"' by '"'
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
(import
replace '(import' by '"__wbg__"'
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
¢
replace '¢' by '(import'
OANAAKRWNHHE (module (type (import "__wbg__" " (@custom "name" "... Celine ) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get
OANAAKRWNHHE (module (type ¢ (import "__wbg__" " (import "__wbg__" " (@custom "name" "... Celine ¢) (func (param i32 i32))) __wbg_log" (func () (type 0))) __wbg_getinput" (func () (type 8))) ")) (type 1) (param i32) (result i32) Clecal i132 i182 132 132 i32) loop 132.eqz tf local.get 0 return end 132.store
example.
delete 'example.' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
code
delete 'code' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
Rust
delete 'Rust' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
the
delete 'the' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
for
delete 'for' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
2.1
delete '2.1' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
Listing
delete 'Listing' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
to
delete 'to' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
Refer
delete 'Refer' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
2.2:
delete '2.2:' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
"...")
delete '"...")' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
"producers"
delete '"producers"' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
(@custom
delete '(@custom' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
args...")
delete 'args...")' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
"invalid
delete '"invalid' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
1048576)
delete '1048576)' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
(132.const
delete '(132.const' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
()
delete '()' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
(data
delete '(data' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
45))
delete '45))' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
(func
delete '(func' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
"main"
delete '"main"' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
(export
delete '(export' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
40))
delete '40))' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
"fibo"
delete '"fibo"' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
0))
delete '0))' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
(memory
delete '(memory' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
"memory"
delete '"memory"' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
1048576))
delete '1048576))' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
(€i32.const
delete '(€i32.const' by ''
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
i32)
replace 'i32)' by '('
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
(mut
replace '(mut' by '1048576))'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
()
replace '()' by '(€i32.const'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
(global
replace '(global' by 'i32)'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
17)
replace '17)' by '(mut'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
insert '' by '(global'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
(memory
replace '(memory' by '17)'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
funcref)
replace 'funcref)' by '()'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
33
replace '33' by '(memory'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
33
replace '33' by 'funcref)'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
()
replace '()' by '33'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
(table
replace '(table' by '33'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
offset=20
replace 'offset=20' by '()'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
i32.store
replace 'i32.store' by '(table'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
40
replace '40' by 'offset=20'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
call
replace 'call' by 'i32.store'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
44
replace '44' by '40'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
insert '' by '44'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
i32)
replace 'i32)' by 'call'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
i32
replace 'i32' by 'i32)'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
insert '' by 'i32'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
¢)
replace '¢)' by ')'
132.store Fy br i ) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) ( This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
132.store Fy br i ¢) end end) (func () (type 8) (result i32) call 2 1i32.store (func () (type 7) (local i32 i32 i32) call 44 call 40 i32.store offset=20 (table () 33 33 funcref) (memory () 17) (global () (mut i32) (€i32.const 1048576)) (export "memory" (memory 0)) (export "fibo" (func 40)) (export "main" (func 45)) (data () (132.const 1048576) "invalid args...") (@custom "producers" "...") Listing 2.2: Refer to Listing 2.1 for the Rust code example. This example showcases the translation from Rust to Wasm. For clarity, we have marked elements and portions of the WebAssembly binary as comments.
2.1
replace '2.1' by '2.1,'
The Wasm binary format is close to machine code and already optimized, being a consecutive collection of sections. In Figure 2.1, we show the binary format of a Wasm section. A Wasm section starts with a 1-byte section ID, followed by a 4-byte section size, and concludes with the section content, which precisely matches the size indicated earlier. A WebAssembly binary contains sections of 13 types, each with a specific semantic role and placement within the module. For instance, the Custom Section stores metadata like the compiler used to generate the binary, while the Type Section contains function signatures that serve to validate the Function Section. The Import Section lists elements imported from the host, and the Function Section details the functions defined within the binary. Other sections like Table, Memory, and Global Sections specify the structure for indirect calls, unmanaged linear memories, and global variables, respectively. Export, Start, Element, Code, Data, and Data Count Sections handle aspects ranging from declaring elements for host engine access to initializing program state, declaring bytecode instructions per function, and initializing linear memory. Each of these sections must occur only once in a binary and can be empty. For clarity, we also annotate sections as comments in the Wasm code in Listing 2.2.
The Wasm binary format is close to machine code and already optimized, being a consecutive collection of sections. In Figure 2.1 we show the binary format of a Wasm section. A Wasm section starts with a 1-byte section ID, followed by a 4-byte section size, and concludes with the section content, which precisely matches the size indicated earlier. A WebAssembly binary contains sections of 13 types, each with a specific semantic role and placement within the module. For instance, the Custom Section stores metadata like the compiler used to generate the binary, while the Type Section contains function signatures that serve to validate the Function Section. The Import Section lists elements imported from the host, and the Function Section details the functions defined within the binary. Other sections like Table, Memory, and Global Sections specify the structure for indirect calls, unmanaged linear memories, and global variables, respectively. Export, Start, Element, Code, Data, and Data Count Sections handle aspects ranging from declaring elements for host engine access to initializing program state, declaring bytecode instructions per function, and initializing linear memory. Each of these sections must occur only once in a binary and can be empty. For clarity, we also annotate sections as comments in the Wasm code in Listing 2.2.
format
replace 'format' by 'format.'
WebAssembly’s binary format is the same as the WebAssembly’s binary format.
WebAssembly’s binary format
insert '' by 'the'
WebAssembly’s binary format is the same as the WebAssembly’s binary format.
WebAssembly’s binary format
insert '' by 'as'
WebAssembly’s binary format is the same as the WebAssembly’s binary format.
WebAssembly’s binary format
insert '' by 'same'
WebAssembly’s binary format is the same as the WebAssembly’s binary format.
WebAssembly’s binary format
insert '' by 'is'
WebAssembly’s binary format is the same as the WebAssembly’s binary format.
WebAssembly’s binary format
insert '' by 'format'
WebAssembly’s binary format is the same as the WebAssembly’s binary format.
WebAssembly’s binary format
insert '' by 'binary'
WebAssembly’s binary format is the same as the WebAssembly’s binary format.
WebAssembly’s binary format
insert '' by 'WebAssembly’s'
WebAssembly’s binary format is the same as the WebAssembly’s binary format.
WebAssembly’s binary format
Javy”
replace 'Javy”' by '“Javy”'
A recent trend in the WebAssembly ecosystem involves porting various programming languages by converting both the language’s engine or interpreter and the source code into a WebAssembly program. For example, “Javy” encapsulates JavaScript code within the QuickJS interpreter, demonstrating that direct source code conversion to WebAssembly isn’t always required. If an interpreter for a specific language can be compiled to WebAssembly, it allows for the bundling of both the interpreter and the language into a single, isolated WebAssembly binary. Similarly, Blazor® facilitates the execution of INET Common Intermediate Language (CIL) in WebAssembly binaries for browser-based applications. However, packaging the interpreter and the code in one single standalone WebAssembly binary is still immature and faces challenges. For example, the absence of JIT compilation for the “interpreted” code makes it less suitable for long-running tasks [46, 47]. On the other hand, it proves effective for short-running tasks, particularly those executed in Edge-Cloud computing platforms.
A recent trend in the WebAssembly ecosystem involves porting various programming languages by converting both the language’s engine or interpreter and the source code into a WebAssembly program. For example, Javy” encapsulates JavaScript code within the QuickJS interpreter, demonstrating that direct source code conversion to WebAssembly isn’t always required. If an interpreter for a specific language can be compiled to WebAssembly, it allows for the bundling of both the interpreter and the language into a single, isolated WebAssembly binary. Similarly, Blazor® facilitates the execution of INET Common Intermediate Language (CIL) in WebAssembly binaries for browser-based applications. However, packaging the interpreter and the code in one single standalone WebAssembly binary is still immature and faces challenges. For example, the absence of JIT compilation for the “interpreted” code makes it less suitable for long-running tasks [46, 47]. On the other hand, it proves effective for short-running tasks, particularly those executed in Edge-Cloud computing platforms.
”
Unpaired symbol: ‘“’ seems to be missing
A recent trend in the WebAssembly ecosystem involves porting various programming languages by converting both the language’s engine or interpreter and the source code into a WebAssembly program. For example, Javy” encapsulates JavaScript code within the QuickJS interpreter, demonstrating that direct source code conversion to WebAssembly isn’t always required. If an interpreter for a specific language can be compiled to WebAssembly, it allows for the bundling of both the interpreter and the language into a single, isolated WebAssembly binary. Similarly, Blazor® facilitates the execution of INET Common Intermediate Language (CIL) in WebAssembly binaries for browser-based applications. However, packaging the interpreter and the code in one single standalone WebAssembly binary is still immature and faces challenges. For example, the absence of JIT compilation for the “interpreted” code makes it less suitable for long-running tasks [46, 47]. On the other hand, it proves effective for short-running tasks, particularly those executed in Edge-Cloud computing platforms.
insert '' by 'Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web'
“https://github.com/bytecodealliance/javy Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web
“https://github.com/bytecodealliance/javy Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor
insert '' by 'Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html'
“https://github.com/bytecodealliance/javy Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web
“https://github.com/bytecodealliance/javy Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor
Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor
replace 'Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor' by 'Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html'
“https://github.com/bytecodealliance/javy Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor.html Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web
“https://github.com/bytecodealliance/javy Shttps://dotnet.microsoft.com/en-us/apps/aspnet/web-apps/blazor
Stack:
delete 'Stack:' by ''
At runtime, WebAssembly engines instantiate a WebAssembly module. This module is a runtime representation of a loaded and initialized WebAssembly binary described in Section 2.1.2. The primary component of a module instance is its Execution Stack. The Execution Stack stores typed values, labels, and control frames. Labels manage block instruction starts and loop starts. Control frames manage function calls and function returns. Values within the stack can only be static types. These types include i32 for 32-bit signed integers, i64 for 64-bit signed integers, £32 for 32-bit floats, and £64 for 64-bit floats. Abstract types such as classes, objects, and arrays are not supported natively. Instead, these types are abstracted into primitive types during compilation and stored in linear memory.
Execution Stack: At runtime, WebAssembly engines instantiate a WebAssembly module. This module is a runtime representation of a loaded and initialized WebAssembly binary described in Section 2.1.2. The primary component of a module instance is its Execution Stack. The Execution Stack stores typed values, labels, and control frames. Labels manage block instruction starts and loop starts. Control frames manage function calls and function returns. Values within the stack can only be static types. These types include i32 for 32-bit signed integers, i64 for 64-bit signed integers, £32 for 32-bit floats, and £64 for 64-bit floats. Abstract types such as classes, objects, and arrays are not supported natively. Instead, these types are abstracted into primitive types during compilation and stored in linear memory.
Execution
delete 'Execution' by ''
At runtime, WebAssembly engines instantiate a WebAssembly module. This module is a runtime representation of a loaded and initialized WebAssembly binary described in Section 2.1.2. The primary component of a module instance is its Execution Stack. The Execution Stack stores typed values, labels, and control frames. Labels manage block instruction starts and loop starts. Control frames manage function calls and function returns. Values within the stack can only be static types. These types include i32 for 32-bit signed integers, i64 for 64-bit signed integers, £32 for 32-bit floats, and £64 for 64-bit floats. Abstract types such as classes, objects, and arrays are not supported natively. Instead, these types are abstracted into primitive types during compilation and stored in linear memory.
Execution Stack: At runtime, WebAssembly engines instantiate a WebAssembly module. This module is a runtime representation of a loaded and initialized WebAssembly binary described in Section 2.1.2. The primary component of a module instance is its Execution Stack. The Execution Stack stores typed values, labels, and control frames. Labels manage block instruction starts and loop starts. Control frames manage function calls and function returns. Values within the stack can only be static types. These types include i32 for 32-bit signed integers, i64 for 64-bit signed integers, £32 for 32-bit floats, and £64 for 64-bit floats. Abstract types such as classes, objects, and arrays are not supported natively. Instead, these types are abstracted into primitive types during compilation and stored in linear memory.
runtime
replace 'runtime' by 'runtime.'
WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.
WebAssembly’s runtime
insert '' by 'as'
WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.
WebAssembly’s runtime
insert '' by 'time'
WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.
WebAssembly’s runtime
insert '' by 'same'
WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.
WebAssembly’s runtime
insert '' by 'the'
WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.
WebAssembly’s runtime
insert '' by 'at'
WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.
WebAssembly’s runtime
insert '' by 'runs'
WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.
WebAssembly’s runtime
insert '' by 'runtime'
WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.
WebAssembly’s runtime
insert '' by 'WebAssembly’s'
WebAssembly’s runtime runs at the same time as WebAssembly’s runtime.
WebAssembly’s runtime
ID2
delete 'ID2' by ''
i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.
i+ i+9 i+9+ Size ID1 Size Content ID2
Content
replace 'Content' by 'ID3.'
i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.
i+ i+9 i+9+ Size ID1 Size Content ID2
insert '' by 'ID2'
i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.
i+ i+9 i+9+ Size ID1 Size Content ID2
ID1
replace 'ID1' by 'Content'
i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.
i+ i+9 i+9+ Size ID1 Size Content ID2
insert '' by 'ID1'
i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.
i+ i+9 i+9+ Size ID1 Size Content ID2
i+9+
replace 'i+9+' by 'Size'
i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.
i+ i+9 i+9+ Size ID1 Size Content ID2
insert '' by 'i+9'
i+ i+9 i+9 Size ID1 Size Content ID2 Size ID3.
i+ i+9 i+9+ Size ID1 Size Content ID2
i
This sentence does not start with an uppercase letter.
i+ i+9 i+9+ Size ID1 Size Content ID2
org
This sentence does not start with an uppercase letter.
°nttps://en.wikipedia. org/wiki/LEB128
Functions:
delete 'Functions:' by ''
At runtime, WebAssembly functions are closures over the module instance, grouping locals and function bodies. Locals are typed variables that are local to a specific function invocation. A function body is a sequence of instructions that are executed when the function is called. Each instruction either reads from the execution stack, writes to the execution stack, reads from the linear memory, writes to the linear memory, reads a global, writes a global or modifies the control-flow of the function. Recalling the example WebAssembly binary, the local variable declarations and typed instructions that are evaluated using the stack can be appreciated between Line 15 and Line 19 in Listing 2.2. When an instruction reads its operands from the stack, it pushes back the result. Notice that, numeric instructions are annotated with their corresponding type.
Functions: At runtime, WebAssembly functions are closures over the module instance, grouping locals and function bodies. Locals are typed variables that are local to a specific function invocation. A function body is a sequence of instructions that are executed when the function is called. Each instruction either reads from the execution stack, writes to the execution stack, reads from the linear memory, writes to the linear memory, reads a global, writes a global or modifies the control-flow of the function. Recalling the example WebAssembly binary, the local variable declarations and typed instructions that are evaluated using the stack can be appreciated between Line 15 and Line 19 in Listing 2.2. When an instruction reads its operands from the stack, it pushes back the result. Notice that, numeric instructions are annotated with their corresponding type.
when
delete 'when' by ''
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
e.g.
delete 'e.g.' by ''
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
level,
replace 'level,' by 'When'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
lowest
replace 'lowest' by 'e.g.'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
the
replace 'the' by 'level,'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
at
replace 'at' by 'lowest'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
engine
replace 'engine' by 'the'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
host
replace 'host' by 'at'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
the
replace 'the' by 'engine'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
by
replace 'by' by 'host'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
modified
replace 'modified' by 'the'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
exclusively
replace 'exclusively' by 'by'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
are
replace 'are' by 'modified'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
that
replace 'that' by 'exclusively'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
components
replace 'components' by 'are'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
to
replace 'to' by 'that'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
refers
replace 'refers' by 'components'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Managed
replace 'Managed' by 'to'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
[20].
replace '[20].' by 'refers'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
memory
replace 'memory' by 'Managed'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
unmanaged
replace 'unmanaged' by '[20].'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
and
replace 'and' by 'memory'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
managed
replace 'managed' by 'unmanaged'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
as
replace 'as' by 'and'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
categorized
replace 'categorized' by 'managed'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
often
replace 'often' by 'as'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
is
replace 'is' by 'categorized'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
responsibility
replace 'responsibility' by 'often'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
of
replace 'of' by 'is'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
division
replace 'division' by 'responsibility'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
This
replace 'This' by 'of'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
itself.
replace 'itself.' by 'division'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
binary
replace 'binary' by 'This'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
the
replace 'the' by 'binary'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
with
replace 'with' by 'WebAssembly'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
shared
replace 'shared' by 'the'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
or
replace 'or' by 'with'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
engine
replace 'engine' by 'shared'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
host
replace 'host' by 'or'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
solely
replace 'solely' by 'the'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
managed
replace 'managed' by 'by'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
be
replace 'be' by 'solely'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
either
replace 'either' by 'managed'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
can
replace 'can' by 'be'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
components
replace 'components' by 'either'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
These
replace 'These' by 'can'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
variables.
replace 'variables.' by 'components'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
global
replace 'global' by 'These'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
and
replace 'and' by 'variables.'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
variables
replace 'variables' by 'global'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
local
replace 'local' by 'and'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
memory,
replace 'memory,' by 'variables'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
linear
replace 'linear' by 'local'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
components:
replace 'components:' by 'memory,'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
memory-related
replace 'memory-related' by 'linear'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
of
replace 'of' by 'components:'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
types
replace 'types' by 'memory-related'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
key
replace 'key' by 'of'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
three
replace 'three' by 'types'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
incorporates
replace 'incorporates' by 'key'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
instance
replace 'instance' by 'three'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
module
replace 'module' by 'incorporates'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
A
replace 'A' by 'module'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
model:
replace 'model:' by 'WebAssembly'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory
replace 'Memory' by 'A'
A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. When the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
Memory model: A WebAssembly module instance incorporates three key types of memory-related components: linear memory, local variables and global variables. These components can either be managed solely by the host engine or shared with the WebAssembly binary itself. This division of responsibility is often categorized as managed and unmanaged memory [20]. Managed refers to components that are exclusively modified by the host engine at the lowest level, e.g. when the WebAssembly binary is JITed, while unmanaged components can also be altered through WebAssembly opcodes. First, modules may include a linear memory instance, which is a contiguous array of bytes. This linear memory is accessed using 32-bit integers (132) and is shareable only between the initiating engine and the WebAssembly module instance. Generally, the linear memory is considered to be unmanaged, e.g., line 28 of Listing 2.2 shows an explicit memory access opcode. Second, there are global instances, which are variables accompanied by values and mutability flags (see example in line 49 of Listing 2.2). These globals are managed by the host engine, which controls their allocation and memory placement completely oblivious to the WebAssembly binary scope. They can only be accessed via their declaration index, prohibiting dynamic addressing. Third, local variables are mutable and specific to a given function instance (e.g., line 15 and line 22 in Listing 2.2). They are accessible only through their index relative to the executing function and are part of the data managed by the host engine.
execution:
delete 'execution:' by ''
While a WebAssembly binary could be interpreted, the most practical approach is to JIT compile it into machine code [48]. The main reason is that WebAssembly is optimized and closely aligned with machine code, leading to swift JIT compilation for execution. Browser engines such as V8!° and SpiderMonkey! use this strategy when executing WebAssembly binaries in browser clients. In practice, browsers initially employ a baseline compiler to ensure the rapid availability of incoming WebAssembly binaries. Simultaneously, an optimizing compiler operates in the background. Consequently, the first generated machine code is eventually supplanted by the optimized version. Once JITed, the WebAssembly binary operates within a sandboxed environment, accessing the host environment exclusively through imported functions. This sandboxing follows the Software Fault Isolation (SFI) guarantee, meaning that a WebAssembly program cannot arbitrarily access code or data of its runtime.
WebAssembly module execution: While a WebAssembly binary could be interpreted, the most practical approach is to JIT compile it into machine code [48]. The main reason is that WebAssembly is optimized and closely aligned with machine code, leading to swift JIT compilation for execution. Browser engines such as V8!° and SpiderMonkey! use this strategy when executing WebAssembly binaries in browser clients. In practice, browsers initially employ a baseline compiler to ensure the rapid availability of incoming WebAssembly binaries. Simultaneously, an optimizing compiler operates in the background. Consequently, the first generated machine code is eventually supplanted by the optimized version. Once JITed, the WebAssembly binary operates within a sandboxed environment, accessing the host environment exclusively through imported functions. This sandboxing follows the Software Fault Isolation (SFI) guarantee, meaning that a WebAssembly program cannot arbitrarily access code or data of its runtime.
module
delete 'module' by ''
While a WebAssembly binary could be interpreted, the most practical approach is to JIT compile it into machine code [48]. The main reason is that WebAssembly is optimized and closely aligned with machine code, leading to swift JIT compilation for execution. Browser engines such as V8!° and SpiderMonkey! use this strategy when executing WebAssembly binaries in browser clients. In practice, browsers initially employ a baseline compiler to ensure the rapid availability of incoming WebAssembly binaries. Simultaneously, an optimizing compiler operates in the background. Consequently, the first generated machine code is eventually supplanted by the optimized version. Once JITed, the WebAssembly binary operates within a sandboxed environment, accessing the host environment exclusively through imported functions. This sandboxing follows the Software Fault Isolation (SFI) guarantee, meaning that a WebAssembly program cannot arbitrarily access code or data of its runtime.
WebAssembly module execution: While a WebAssembly binary could be interpreted, the most practical approach is to JIT compile it into machine code [48]. The main reason is that WebAssembly is optimized and closely aligned with machine code, leading to swift JIT compilation for execution. Browser engines such as V8!° and SpiderMonkey! use this strategy when executing WebAssembly binaries in browser clients. In practice, browsers initially employ a baseline compiler to ensure the rapid availability of incoming WebAssembly binaries. Simultaneously, an optimizing compiler operates in the background. Consequently, the first generated machine code is eventually supplanted by the optimized version. Once JITed, the WebAssembly binary operates within a sandboxed environment, accessing the host environment exclusively through imported functions. This sandboxing follows the Software Fault Isolation (SFI) guarantee, meaning that a WebAssembly program cannot arbitrarily access code or data of its runtime.
15https://github.com/WAVM/WAVM
replace '15https://github.com/WAVM/WAVM' by '//github.com'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
//github.com/bytecodealliance/wasmtime
replace '//github.com/bytecodealliance/wasmtime' by '12ntps:'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
M4nttps:
replace 'M4nttps:' by 'llhttps://spidermonkey.dev/'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
13https://wasmer.io/
replace '13https://wasmer.io/' by '15https://github.com/WAVM/WAVM.git'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
//github.com/wasm3/wasm3
replace '//github.com/wasm3/wasm3' by '//github.com/bytecodealliance/wasmtime'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
12nttps:
replace '12nttps:' by 'M4ntps:'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
llhttps://spidermonkey.dev/
replace 'llhttps://spidermonkey.dev/' by '13https://wasmer.io/'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
googlesource.com/v8/v8.git
replace 'googlesource.com/v8/v8.git' by '//github.com/wasm3/wasm3'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
//chromium.
replace '//chromium.' by '12ntps:'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
10nttps:
replace '10nttps:' by 'llhttps://spidermonkey.dev/'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
WASI.
replace 'WASI.' by 'Googlesource.com/v8/v8.git'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
and
replace 'and' by '//chromium.'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
both
replace 'both' by 'WASI.'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
support
replace 'support' by 'and'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
to
replace 'to' by 'WebAssembly'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
developed
replace 'developed' by 'both'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
been
replace 'been' by 'support'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
have
replace 'have' by 'to'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
[50]
replace '[50]' by 'developed'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
Sledge
replace 'Sledge' by 'been'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
and
replace 'and' by 'have'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
WAVM",
replace 'WAVM",' by '[50]'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
Wasmtime!4,
replace 'Wasmtime!4,' by 'Sledge'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
Wasmer!*,
replace 'Wasmer!*,' by 'and'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
WASM3!?,
replace 'WASM3!?,' by 'WAVM"'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
as
replace 'as' by 'Wasmtime!4,'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
such
replace 'such' by 'Wasmer!*,'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
engines
replace 'engines' by 'WASM3!?,'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
Standalone
replace 'Standalone' by 'as'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
process.
replace 'process.' by 'such'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
execution
replace 'execution' by 'engines'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
sandboxed
replace 'sandboxed' by 'Standalone'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
a
replace 'a' by 'process.'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
ensuring
replace 'ensuring' by 'execution'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
compilation,
replace 'compilation,' by 'sandboxed'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
JIT
replace 'JIT' by 'a'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
via
replace 'via' by 'ensuring'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
code
replace 'code' by 'compilation,'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
machine
replace 'machine' by 'JIT'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
into
replace 'into' by 'via'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
translate
replace 'translate' by 'machine'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
often
replace 'often' by 'into'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
engines
replace 'engines' by 'WebAssembly'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
these
replace 'these' by 'translate'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
browsers,
replace 'browsers,' by 'often'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
to
replace 'to' by 'engines'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
Similarly
replace 'Similarly' by 'these'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
48].
replace '48].' by 'browsers,'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
[49,
replace '[49,' by 'to'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
devices
replace 'devices' by 'Similarly'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
IoT
replace 'IoT' by '48].'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
and
replace 'and' by '[49,'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
servers,
replace 'servers,' by 'devices'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
cloud,
replace 'cloud,' by 'IoT'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
the
replace 'the' by 'and'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
including
replace 'including' by 'servers,'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
environments,
replace 'environments,' by 'cloud,'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
of
replace 'of' by 'the'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
variety
replace 'variety' by 'including'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
a
replace 'a' by 'environments,'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
across
replace 'across' by 'of'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
engines
replace 'engines' by 'variety'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
standalone
replace 'standalone' by 'a'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
by
replace 'by' by 'across'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
executed
replace 'executed' by 'engines'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
be
replace 'be' by 'standalone'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
then
replace 'then' by 'by'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
can
replace 'can' by 'executed'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
binaries
replace 'binaries' by 'be'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
These
replace 'These' by 'then'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
engines.
replace 'engines.' by 'can'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
standalone
replace 'standalone' by 'binaries'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
in
replace 'in' by 'These'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
execution
replace 'execution' by 'engines.'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
allows
replace 'allows' by 'standalone'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
which
replace 'which' by 'in'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
WASI,
replace 'WASI,' by 'execution'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
implement
replace 'implement' by 'allows'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
that
replace 'that' by 'which'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
binaries
replace 'binaries' by 'WASI,'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
generate
replace 'generate' by 'that'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
can
replace 'can' by 'binaries'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
Compilers
replace 'Compilers' by 'WebAssembly'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
environments.
replace 'environments.' by 'generate'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
host
replace 'host' by 'can'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
and
replace 'and' by 'Compilers'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
modules
replace 'modules' by 'environments.'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
between
replace 'between' by 'and'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
interactions
replace 'interactions' by 'modules'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
for
replace 'for' by 'WebAssembly'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
interface
replace 'interface' by 'between'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
POSIX-like
replace 'POSIX-like' by 'interactions'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
standardized
replace 'standardized' by 'for'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
a
replace 'a' by 'interface'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
establishes
replace 'establishes' by 'POSIX-like'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
[41].
replace '[41].' by 'a'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
to
replace 'to' by 'WASI'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
due
replace 'due' by '[41].'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
primarily
replace 'primarily' by 'WASI'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
evolution,
replace 'evolution,' by 'to'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
significant
replace 'significant' by 'due'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
undergone
replace 'undergone' by 'primarily'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
has
replace 'has' by 'evolution,'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
browsers,
replace 'browsers,' by 'undergone'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
for
replace 'for' by 'has'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
intended
replace 'intended' by 'WebAssembly'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
initially
replace 'initially' by 'browsers,'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
While
replace 'While' by 'for'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
engines:
replace 'engines:' by 'intended'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
standalone
replace 'standalone' by 'initially'
While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM" and Sledge [50] have been developed to support both WebAssembly and WASI. 10ntps: //chromium. Googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12ntps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4ntps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM.git llhttps://spidermonkey.dev/ 12ntps: //github.com
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
"
Unpaired symbol: ‘"’ seems to be missing
WebAssembly standalone engines: While initially intended for browsers, WebAssembly has undergone significant evolution, primarily due to WASI [41]. WASI establishes a standardized POSIX-like interface for interactions between WebAssembly modules and host environments. Compilers can generate WebAssembly binaries that implement WASI, which allows execution in standalone engines. These binaries can then be executed by standalone engines across a variety of environments, including the cloud, servers, and IoT devices [49, 48]. Similarly to browsers, these engines often translate WebAssembly into machine code via JIT compilation, ensuring a sandboxed execution process. Standalone engines such as WASM3!?, Wasmer!*, Wasmtime!4, WAVM", and Sledge [50] have been developed to support both WebAssembly and WASI. 10nttps: //chromium. googlesource.com/v8/v8.git llhttps://spidermonkey.dev/ 12nttps: //github.com/wasm3/wasm3 13https://wasmer.io/ M4nttps: //github.com/bytecodealliance/wasmtime 15https://github.com/WAVM/WAVM
end
delete 'end' by ''
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
instruction;
replace 'instruction;' by 'instruction.'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
insert '' by 'the'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
insert '' by 'instruction;'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
insert '' by 'next'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
insert '' by 'to'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
insert '' by 'jump'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
insert '' by 'and'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
insert '' by 'block'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
insert '' by ';)'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
insert '' by 'Jump'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
insert '' by 'ena'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
insert '' by 'break'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
;
replace ';' by 'instructions'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
insert '' by ';'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
they
replace 'they' by 'end'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
DLoCk
replace 'DLoCk' by 'they'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
oe
replace 'oe' by 'DLoCk'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
wees
replace 'wees' by 'oe'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
eeay
replace 'eeay' by 'wees'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
oniee
replace 'oniee' by 'eeay'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
paint
replace 'paint' by 'oniee'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
0
replace '0' by 'paint'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
br
replace 'br' by '0'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
the
replace 'the' by 'br'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
with
replace 'with' by 'the'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
annotated
replace 'annotated' by 'with'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
are
replace 'are' by 'annotated'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
a
replace 'a' by 'are'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
instructions
replace 'instructions' by 'a'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
Jump
replace 'Jump' by 'instructions'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
;
replace ';' by 'Jump'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
1
replace '1' by ';'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
br
replace 'br' by '1'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
Toop
replace 'Toop' by 'br'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
block
replace 'block' by 'Toop'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
insert '' by 'Block'
Block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to the next instruction; end instructions break ena Jump ;) the block and jump to the next instruction.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
block
This sentence does not start with an uppercase letter.
block block Toop br 1 ; Jump instructions a are annotated with the br 0 paint oniee eeay wees oe DLoCk they end ; end instructions break ena Jump ;) the block and jump to next instruction; end
to
delete 'to' by ''
A WebAssembly function groups instructions into blocks, with the function’s entrypoint acting as the root block. In contrast to conventional assembly code, control-flow structures in Wasm leap between block boundaries rather than arbitrary positions within the code, effectively prohibiting random code positions. Each block may specify the needed execution stack state before execution as well as the resultant execution stack state once its instructions have been executed. Typically, the execution stack state is the quantity and numeric type of values on the stack. This stack state is used to validate the binary during compilation and to ensure that the stack is in a valid state before the execution of the block’s instructions. Blocks in Wasm are explicit (see instructions block and end in lines 16 and 34 of Listing 2.2), delineating where they start and end. By design, a block cannot reference or execute code from external blocks.
A WebAssembly function groups instructions into blocks, with the function’s entrypoint acting as the root block. In contrast to conventional assembly code, control-flow structures in Wasm leap between block boundaries rather than arbitrary positions within the code, effectively prohibiting gotos to random code positions. Each block may specify the needed execution stack state before execution as well as the resultant execution stack state once its instructions have been executed. Typically, the execution stack state is the quantity and numeric type of values on the stack. This stack state is used to validate the binary during compilation and to ensure that the stack is in a valid state before the execution of the block’s instructions. Blocks in Wasm are explicit (see instructions block and end in lines 16 and 34 of Listing 2.2), delineating where they start and end. By design, a block cannot reference or execute code from external blocks.
gotos
delete 'gotos' by ''
A WebAssembly function groups instructions into blocks, with the function’s entrypoint acting as the root block. In contrast to conventional assembly code, control-flow structures in Wasm leap between block boundaries rather than arbitrary positions within the code, effectively prohibiting random code positions. Each block may specify the needed execution stack state before execution as well as the resultant execution stack state once its instructions have been executed. Typically, the execution stack state is the quantity and numeric type of values on the stack. This stack state is used to validate the binary during compilation and to ensure that the stack is in a valid state before the execution of the block’s instructions. Blocks in Wasm are explicit (see instructions block and end in lines 16 and 34 of Listing 2.2), delineating where they start and end. By design, a block cannot reference or execute code from external blocks.
A WebAssembly function groups instructions into blocks, with the function’s entrypoint acting as the root block. In contrast to conventional assembly code, control-flow structures in Wasm leap between block boundaries rather than arbitrary positions within the code, effectively prohibiting gotos to random code positions. Each block may specify the needed execution stack state before execution as well as the resultant execution stack state once its instructions have been executed. Typically, the execution stack state is the quantity and numeric type of values on the stack. This stack state is used to validate the binary during compilation and to ensure that the stack is in a valid state before the execution of the block’s instructions. Blocks in Wasm are explicit (see instructions block and end in lines 16 and 34 of Listing 2.2), delineating where they start and end. By design, a block cannot reference or execute code from external blocks.
insert '' by 'This'
The WebAssembly ecosystem’s expansion needs robust tools to ensure its security and reliability. Numerous tools, employing various strategies to detect vulnerabilities in WebAssembly programs, have been created to meet this need. This section reviews the most relevant works in this field. We group them by the technique they employ.
The WebAssembly ecosystem’s expansion needs robust tools to ensure its security and reliability. Numerous tools, employing various strategies to detect vulnerabilities in WebAssembly programs, have been created to meet this need.This section reviews the most relevant works in this field. We group them by the technique they employ.
need.This
replace 'need.This' by 'need.'
The WebAssembly ecosystem’s expansion needs robust tools to ensure its security and reliability. Numerous tools, employing various strategies to detect vulnerabilities in WebAssembly programs, have been created to meet this need. This section reviews the most relevant works in this field. We group them by the technique they employ.
The WebAssembly ecosystem’s expansion needs robust tools to ensure its security and reliability. Numerous tools, employing various strategies to detect vulnerabilities in WebAssembly programs, have been created to meet this need.This section reviews the most relevant works in this field. We group them by the technique they employ.
analysis:
delete 'analysis:' by ''
SecWasm [52] uses information control-flow checking to identify secrecy leaking in WebAssembly binaries. Similarly, Wasmati [53] employs code property graphs for this purpose. Wasp [54] leverages concolic execution to identify potential vulnerabilities in WebAssembly binaries. CT-Wasm [55], verifies the constant time implementation of cryptographic algorithms in WebAssembly. Similarly, Vivienne applies relational symbolic execution to WebAssembly binaries in order to reveal vulnerabilities in cryptographic implementations [56]. While these tools emphasize specific strategies, others adopt a more holistic approach. For example, both Wassail [57] and WasmA [58] provide a comprehensive static analysis framework for WebAssembly binaries.
Static analysis: SecWasm [52] uses information control-flow checking to identify secrecy leaking in WebAssembly binaries. Similarly, Wasmati [53] employs code property graphs for this purpose. Wasp [54] leverages concolic execution to identify potential vulnerabilities in WebAssembly binaries. CT-Wasm [55], verifies the constant time implementation of cryptographic algorithms in WebAssembly. Similarly, Vivienne applies relational symbolic execution to WebAssembly binaries in order to reveal vulnerabilities in cryptographic implementations [56]. While these tools emphasize specific strategies, others adopt a more holistic approach. For example, both Wassail [57] and WasmA [58] provide a comprehensive static analysis framework for WebAssembly binaries.
Static
delete 'Static' by ''
SecWasm [52] uses information control-flow checking to identify secrecy leaking in WebAssembly binaries. Similarly, Wasmati [53] employs code property graphs for this purpose. Wasp [54] leverages concolic execution to identify potential vulnerabilities in WebAssembly binaries. CT-Wasm [55], verifies the constant time implementation of cryptographic algorithms in WebAssembly. Similarly, Vivienne applies relational symbolic execution to WebAssembly binaries in order to reveal vulnerabilities in cryptographic implementations [56]. While these tools emphasize specific strategies, others adopt a more holistic approach. For example, both Wassail [57] and WasmA [58] provide a comprehensive static analysis framework for WebAssembly binaries.
Static analysis: SecWasm [52] uses information control-flow checking to identify secrecy leaking in WebAssembly binaries. Similarly, Wasmati [53] employs code property graphs for this purpose. Wasp [54] leverages concolic execution to identify potential vulnerabilities in WebAssembly binaries. CT-Wasm [55], verifies the constant time implementation of cryptographic algorithms in WebAssembly. Similarly, Vivienne applies relational symbolic execution to WebAssembly binaries in order to reveal vulnerabilities in cryptographic implementations [56]. While these tools emphasize specific strategies, others adopt a more holistic approach. For example, both Wassail [57] and WasmA [58] provide a comprehensive static analysis framework for WebAssembly binaries.
malware:
delete 'malware:' by ''
Since the introduction of WebAssembly, the Web has consistently experienced an increase in cryptomalware. This rise primarily stems from the shift of mining algorithms from CPUs to WebAssembly, a transition driven by notable performance benefits [66]. Tools such as MineSweeper [26], MinerRay [27], and MINOS [28] employ static analysis with machine learning techniques to detect browser-based cryptomalware. In addition, SEISMIC [29], RAPID [30], and OUTGuard [31] leverage dynamic analysis techniques to achieve a similar objective. Moreover, VirusTotal’®, a tool incorporating over 60 commercial antivirus systems as black-boxes, is capable of detecting cryptomalware in WebAssembly binaries. However, obfuscation studies have exposed their shortcomings, revealing an almost unexplored area for WebAssembly that threatens malware detection accuracy. In concrete, Bhansali et al.’s seminal work [67] demonstrates that cryptomining algorithm’s source code can evade previous techniques through the use of obfuscation techniques.
WebAssembly malware: Since the introduction of WebAssembly, the Web has consistently experienced an increase in cryptomalware. This rise primarily stems from the shift of mining algorithms from CPUs to WebAssembly, a transition driven by notable performance benefits [66]. Tools such as MineSweeper [26], MinerRay [27], and MINOS [28] employ static analysis with machine learning techniques to detect browser-based cryptomalware. In addition, SEISMIC [29], RAPID [30], and OUTGuard [31] leverage dynamic analysis techniques to achieve a similar objective. Moreover, VirusTotal’®, a tool incorporating over 60 commercial antivirus systems as black-boxes, is capable of detecting cryptomalware in WebAssembly binaries. However, obfuscation studies have exposed their shortcomings, revealing an almost unexplored area for WebAssembly that threatens malware detection accuracy. In concrete, Bhansali et al.’s seminal work [67] demonstrates that cryptomining algorithm’s source code can evade previous techniques through the use of obfuscation techniques.
’
Unpaired symbol: ‘‘’ seems to be missing
WebAssembly malware: Since the introduction of WebAssembly, the Web has consistently experienced an increase in cryptomalware. This rise primarily stems from the shift of mining algorithms from CPUs to WebAssembly, a transition driven by notable performance benefits [66]. Tools such as MineSweeper [26], MinerRay [27], and MINOS [28] employ static analysis with machine learning techniques to detect browser-based cryptomalware. In addition, SEISMIC [29], RAPID [30], and OUTGuard [31] leverage dynamic analysis techniques to achieve a similar objective. Moreover, VirusTotal’®, a tool incorporating over 60 commercial antivirus systems as black-boxes, is capable of detecting cryptomalware in WebAssembly binaries. However, obfuscation studies have exposed their shortcomings, revealing an almost unexplored area for WebAssembly that threatens malware detection accuracy. In concrete, Bhansali et al.’s seminal work [67] demonstrates that cryptomining algorithm’s source code can evade previous techniques through the use of obfuscation techniques.
therefore
delete 'therefore' by ''
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
and
delete 'and' by ''
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
execution,
delete 'execution,' by ''
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
secure
delete 'secure' by ''
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
their
delete 'their' by ''
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
enhancing
replace 'enhancing' by 'thus'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
binaries,
replace 'binaries,' by 'and'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
harden
replace 'harden' by 'secure'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
to
replace 'to' by 'their'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
approaches
replace 'approaches' by 'enhancing'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
exist
replace 'exist' by 'binaries,'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
there
replace 'there' by 'WebAssembly'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
However,
replace 'However,' by 'harden'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
binaries.
replace 'binaries.' by 'to'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
of
replace 'of' by 'are'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
analysis
replace 'analysis' by 'there'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
reactive
replace 'reactive' by 'However,'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
on
replace 'on' by 'binaries.'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
focused
replace 'focused' by 'WebAssembly'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
primarily
replace 'primarily' by 'of'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
are
replace 'are' by 'analysis'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
previously
replace 'previously' by 'reactive'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
discussed
replace 'discussed' by 'on'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
techniques
replace 'techniques' by 'focused'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
The
replace 'The' by 'primarily'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
runtimes:
replace 'runtimes:' by 'are'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
and
replace 'and' by 'previously'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
binaries
replace 'binaries' by 'discussed'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting
replace 'Protecting' by 'The'
The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there are approaches to harden WebAssembly binaries, enhancing their secure execution, and thus protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
Protecting WebAssembly binaries and runtimes: The techniques discussed previously are primarily focused on reactive analysis of WebAssembly binaries. However, there exist approaches to harden WebAssembly binaries, enhancing their secure execution, and therefore protecting the security of the entire execution ecosystem. For instance, Swivel [63] proposes a compiler-based strategy designed to eliminate speculative attacks on WebAssembly binaries in Function-as-a-Service (FaaS) platforms by linearizing the machine code from compiling a WebAssembly binary. Similarly, Kolosick et al. [64] modify the Lucet compiler to use zero-cost transitions, eliminating the performance overhead of SFI guarantees implementation. In addition, WaVe [65] introduces a mechanized engine for WebAssembly that facilitates differential testing. WaVe can be employed to detect anomalies in engine implementations running Wasm-WASI programs.
‘These
delete '‘These' by ''
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
code.
delete 'code.' by ''
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
debugging
replace 'debugging' by 'code.'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
and
replace 'and' by 'WebAssembly'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
instrumenting,
replace 'instrumenting,' by 'debugging'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
profiling,
replace 'profiling,' by 'and'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
for
replace 'for' by 'instrumenting,'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
used
replace 'used' by 'profiling,'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
primarily
replace 'primarily' by 'for'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
binaries,
replace 'binaries,' by 'used'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
for
replace 'for' by 'binaries,'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
tool
replace 'tool' by 'WebAssembly'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
analysis
replace 'analysis' by 'for'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
dynamic
replace 'dynamic' by 'tool'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
general-purpose
replace 'general-purpose' by 'analysis'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
only
replace 'only' by 'dynamic'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
the
replace 'the' by 'general-purpose'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
remains
replace 'remains' by 'only'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
[62]
replace '[62]' by 'the'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
Wasabi
replace 'Wasabi' by 'remains'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
However,
replace 'However,' by '[62]'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
analysis.
replace 'analysis.' by 'Wasabi'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
security
replace 'security' by 'However,'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
conducting
replace 'conducting' by 'analysis.'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
and
replace 'and' by 'security'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
programs,
replace 'programs,' by 'conducting'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
understanding
replace 'understanding' by 'and'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
debugging,
replace 'debugging,' by 'programs,'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
in
replace 'in' by 'understanding'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
aids
replace 'aids' by 'debugging,'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
technique
replace 'technique' by 'in'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
This
replace 'This' by 'aids'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
61].
replace '61].' by 'technique'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
(ORBS)|[60,
replace '(ORBS)|[60,' by 'This'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
Slicing
replace 'Slicing' by '61].'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
Observational-Based
replace 'Observational-Based' by '(ORBS)|[60,'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
on
replace 'on' by 'Slicing'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
based
replace 'based' by 'Observational-Based'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
programs
replace 'programs' by 'on'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
slicing
replace 'slicing' by 'programs'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
to
replace 'to' by 'WebAssembly'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
approach
replace 'approach' by 'slicing'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
dynamic
replace 'dynamic' by 'to'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
a
replace 'a' by 'approach'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
developed
replace 'developed' by 'dynamic'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
have
replace 'have' by 'a'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
al.
replace 'al.' by 'developed'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
et
replace 'et' by 'Have'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
Stiévenart
replace 'Stiévenart' by 'al.'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
Furthermore,
replace 'Furthermore,' by 'et'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
binaries.
replace 'binaries.' by 'Stiévenart'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
on
replace 'on' by 'binaries.'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
analysis
replace 'analysis' by 'WebAssembly'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
taint
replace 'taint' by 'on'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
conducts
replace 'conducts' by 'analysis'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
which
replace 'which' by 'taint'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
[59],
replace '[59],' by 'conducts'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
TaintAssembly
replace 'TaintAssembly' by 'which'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
as
replace 'as' by '[59],'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
such
replace 'such' by 'TaintAssembly'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
tools
replace 'tools' by 'as'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
involves
replace 'involves' by 'such'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
analysis
replace 'analysis' by 'tools'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic
replace 'Dynamic' by 'involves'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
analysis:
replace 'analysis:' by 'analysis'
Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. Have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. These tools typically analyze software behavior during execution, making them inherently reactive.
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
‘
Unpaired symbol: ‘’’ seems to be missing
Dynamic analysis: Dynamic analysis involves tools such as TaintAssembly [59], which conducts taint analysis on WebAssembly binaries. Furthermore, Stiévenart et al. have developed a dynamic approach to slicing WebAssembly programs based on Observational-Based Slicing (ORBS)|[60, 61]. This technique aids in debugging, understanding programs, and conducting security analysis. However, Wasabi [62] remains the only general-purpose dynamic analysis tool for WebAssembly binaries, primarily used for profiling, instrumenting, and debugging WebAssembly code. ‘These tools typically analyze software behavior during execution, making them inherently reactive.
diversification
replace 'diversification' by 'diversification.'
Software diversification.
Software diversification
al.
replace 'al.' by 'was'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
et
replace 'et' by 'It'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Stiévenart
replace 'Stiévenart' by 'al.'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
and
replace 'and' by 'et'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
al.
replace 'al.' by 'Stiévenart'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
et
replace 'et' by 'al.'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Lehmann
replace 'Lehmann' by 'et'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
by
replace 'by' by 'Lehmann'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
studies
replace 'studies' by 'by'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
example,
replace 'example,' by 'studies'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
For
replace 'For' by 'example,'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
vulnerable.
replace 'vulnerable.' by 'For'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
inherently
replace 'inherently' by 'vulnerable.'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
be
replace 'be' by 'inherently'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
may
replace 'may' by 'be'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
themselves
replace 'themselves' by 'may'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
binaries
replace 'binaries' by 'themselves'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
the
replace 'the' by 'binaries'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Finally,
replace 'Finally,' by 'the'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
[18].
replace '[18].' by 'Finally,'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
browsers
replace 'browsers' by '[18].'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
in
replace 'in' by 'browsers'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
binaries
replace 'binaries' by 'in'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
on
replace 'on' by 'WebAssembly'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
attacks
replace 'attacks' by 'on'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
side-channel
replace 'side-channel' by 'attacks'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
contention
replace 'contention' by 'side-channel'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
port
replace 'port' by 'contention'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
for
replace 'for' by 'port'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
possibility
replace 'possibility' by 'for'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
the
replace 'the' by 'possibility'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
disclosed
replace 'disclosed' by 'The'
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. The possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. Stiévenart et al. It was suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
Despite progress in WebAssembly analysis, numerous challenges remain. WebAssembly, though deterministic and well-typed by design, is susceptible to a variety of security threats. First, most existing WebAssembly research is reactive, focusing on detecting and fixing vulnerabilities already reported. This approach leaves WebAssembly binaries and runtime implementations potentially open to unidentified attacks. Second, side-channel attacks present a significant risk. Genkin et al., for example, illustrated how WebAssembly could be manipulated to extract data via cache timing-side channels [22]. Furthermore, research conducted by Maisuradze and Rossow demonstrated the potential for speculative execution attacks on WebAssembly binaries [23]. Rokicki et al. disclosed the possibility for port contention side-channel attacks on WebAssembly binaries in browsers [18]. Finally, the binaries themselves may be inherently vulnerable. For example, studies by Lehmann et al. and Stiévenart et al. suggested that flaws in C/C++ source code could infiltrate WebAssembly binaries [20, 21].
challenges
replace 'challenges' by 'challenges.'
Open challenges.
Open challenges
variants
replace 'variants' by 'variants.'
Automatic generation of software variants.
Automatic generation of software variants
instr1i
Possible typo detected.
The concept of automatic software variants starts with Randell’s 1975 work [79], which put forth the notion of artificial fault-tolerant instruction blocks. Artificial Software Diversification, as proposed by Cohen and Forrest in the 1990s [33, 34], gets its development through rewriting strategies. These strategies consist of sets of rewriting rules for modifying software components to create functionally equivalent, yet distinct, programs. Rewriting strategies typically take the form of tuples: instr1i => (instr2, instr3,...), where instr represents the original code and (instr2, instr3,...) denotes the functionally equivalent code.
Reordering:
replace 'Reordering:' by 'Reordering'
Instruction Reordering This strategy reorders instructions in a program. For example, variable declarations may change if compilers reorder them in the symbol tables. This prevents static examination and analysis of parameters and alters memory locations. In this area, Bhatkar et al. [83, 84] proposed the random permutation of variable and routine order for ELF binaries. Such strategies are not implemented for WebAssembly to the best of our knowledge.
Instruction Reordering: This strategy reorders instructions in a program. For example, variable declarations may change if compilers reorder them in the symbol tables. This prevents static examination and analysis of parameters and alters memory locations. In this area, Bhatkar et al. [83, 84] proposed the random permutation of variable and routine order for ELF binaries. Such strategies are not implemented for WebAssembly to the best of our knowledge.
strategy:
delete 'strategy:' by ''
The automatic creation of Software Diversification begins with creating rewriting rules. A rewriting rule refers to a functionally equivalent substitution for a code segment, manually written. These rules can be applied at varying levels, from coarse to fine-grained. This can range from the program dependencies level [80] to the instruction level [75]. For example, Cleemput et al. [81] and Homescu et al. [82] inject NOP instructions to yield statically varied versions at the instruction level. Here, the rewriting rule is represented as instr => (nop instr), signifying an insertion of a nop operation preceding the instruction.
Rewriting strategy: The automatic creation of Software Diversification begins with creating rewriting rules. A rewriting rule refers to a functionally equivalent substitution for a code segment, manually written. These rules can be applied at varying levels, from coarse to fine-grained. This can range from the program dependencies level [80] to the instruction level [75]. For example, Cleemput et al. [81] and Homescu et al. [82] inject NOP instructions to yield statically varied versions at the instruction level. Here, the rewriting rule is represented as instr => (nop instr), signifying an insertion of a nop operation preceding the instruction.
Rewriting
delete 'Rewriting' by ''
The automatic creation of Software Diversification begins with creating rewriting rules. A rewriting rule refers to a functionally equivalent substitution for a code segment, manually written. These rules can be applied at varying levels, from coarse to fine-grained. This can range from the program dependencies level [80] to the instruction level [75]. For example, Cleemput et al. [81] and Homescu et al. [82] inject NOP instructions to yield statically varied versions at the instruction level. Here, the rewriting rule is represented as instr => (nop instr), signifying an insertion of a nop operation preceding the instruction.
Rewriting strategy: The automatic creation of Software Diversification begins with creating rewriting rules. A rewriting rule refers to a functionally equivalent substitution for a code segment, manually written. These rules can be applied at varying levels, from coarse to fine-grained. This can range from the program dependencies level [80] to the instruction level [75]. For example, Cleemput et al. [81] and Homescu et al. [82] inject NOP instructions to yield statically varied versions at the instruction level. Here, the rewriting rule is represented as instr => (nop instr), signifying an insertion of a nop operation preceding the instruction.
implement
replace 'implement' by 'Implement'
ISA Randomization and Simulation: This strategy involves using a key to cipher the original program binary into another encoded binary. Once encoded, the program can only be decoded at the target client, or it can be interpreted in the encoded form using a custom virtual machine implementation. This technique is strong against attacks involving code inspection. Kc et al. [92], and Barrantes et al. [93] proposed seminal works on instruction-set randomization to create a unique mapping between artificial CPU instructions and real ones. On the same topic, Chew and Song [94] target operating system randomization. They randomize the interface between the operating system and the user applications. Couroussé et al. [95] Implement an assembly-like DSL to generate equivalent code at runtime in order to increase protection against side-channel attacks. Their technique generates a different program during execution using an interpreter for their DSL. Generally, [SA randomization and simulation usually faces a performance penalty, especially for WebAssembly, due to the decoding process as shown in WASMixer evaluation [96].
ISA Randomization and Simulation: This strategy involves using a key to cipher the original program binary into another encoded binary. Once encoded, the program can only be decoded at the target client, or it can be interpreted in the encoded form using a custom virtual machine implementation. This technique is strong against attacks involving code inspection. Kc et al. [92], and Barrantes et al. [93] proposed seminal works on instruction-set randomization to create a unique mapping between artificial CPU instructions and real ones. On the same topic, Chew and Song [94] target operating system randomization. They randomize the interface between the operating system and the user applications. Couroussé et al. [95] implement an assembly-like DSL to generate equivalent code at runtime in order to increase protection against side-channel attacks. Their technique generates a different program during execution using an interpreter for their DSL. Generally, [SA randomization and simulation usually faces a performance penalty, especially for WebAssembly, due to the decoding process as shown in WASMixer evaluation [96].
[
Unpaired symbol: ‘]’ seems to be missing
ISA Randomization and Simulation: This strategy involves using a key to cipher the original program binary into another encoded binary. Once encoded, the program can only be decoded at the target client, or it can be interpreted in the encoded form using a custom virtual machine implementation. This technique is strong against attacks involving code inspection. Kc et al. [92], and Barrantes et al. [93] proposed seminal works on instruction-set randomization to create a unique mapping between artificial CPU instructions and real ones. On the same topic, Chew and Song [94] target operating system randomization. They randomize the interface between the operating system and the user applications. Couroussé et al. [95] implement an assembly-like DSL to generate equivalent code at runtime in order to increase protection against side-channel attacks. Their technique generates a different program during execution using an interpreter for their DSL. Generally, [SA randomization and simulation usually faces a performance penalty, especially for WebAssembly, due to the decoding process as shown in WASMixer evaluation [96].
Yet,
delete 'Yet,' by ''
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
functions.
delete 'functions.' by ''
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
complementary
delete 'complementary' by ''
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
to
replace 'to' by 'the'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
calls
replace 'calls' by 'Yet,'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
by
replace 'by' by 'functions.'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
replaced
replace 'replaced' by 'WebAssembly'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
is
replace 'is' by 'complementary'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
code
replace 'code' by 'to'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
the
replace 'the' by 'calls'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
of
replace 'of' by 'by'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
part
replace 'part' by 'replaced'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
which
replace 'which' by 'is'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
in
replace 'in' by 'code'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
JavaScript
replace 'JavaScript' by 'the'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
for
replace 'for' by 'of'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
wobfuscator,
replace 'wobfuscator,' by 'part'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
technique,
replace 'technique,' by 'which'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
obfuscation
replace 'obfuscation' by 'in'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
an
replace 'an' by 'JavaScript'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
proposed
replace 'proposed' by 'for'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
[87]
replace '[87]' by 'wobfuscator,'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
al.
replace 'al.' by 'technique,'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
et
replace 'et' by 'obfuscation'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
Romano
replace 'Romano' by 'an'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
WebAssembly,
replace 'WebAssembly,' by 'proposed'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
of
replace 'of' by '[87]'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
context
replace 'context' by 'al.'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
the
replace 'the' by 'et'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
In
replace 'In' by 'Romano'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
programs.
replace 'programs.' by 'WebAssembly,'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
insert '' by 'context'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
engineering
replace 'engineering' by 'the'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
reverse
replace 'reverse' by 'In'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
static
replace 'static' by 'programs.'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
tackle
replace 'tackle' by 'of'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
to
replace 'to' by 'engineering'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
aim
replace 'aim' by 'reverse'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
strategies
replace 'strategies' by 'static'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
both
replace 'both' by 'tackle'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
Yet,
replace 'Yet,' by 'to'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
[97].
replace '[97].' by 'aim'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
client
replace 'client' by 'strategies'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
any
replace 'any' by 'both'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
in
replace 'in' by 'Yet,'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
is
replace 'is' by '[97].'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
as
replace 'as' by 'client'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
executes
replace 'executes' by 'any'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
latter
replace 'latter' by 'in'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
the
replace 'the' by 'is'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
while
replace 'while' by 'as'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
key
replace 'key' by 'executes'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
encoding
replace 'encoding' by 'latter'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
know
delete 'know' by ''
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
to
replace 'to' by 'while'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
target
replace 'target' by 'key'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
final
replace 'final' by 'encoding'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
insert '' by 'know'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
insert '' by 'to'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
requires
replace 'requires' by 'target'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
former
replace 'former' by 'final'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
insert '' by 'requires'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
that
replace 'that' by 'latter'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
is
replace 'is' by 'the'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
code
replace 'code' by 'that'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
obfuscating
replace 'obfuscating' by 'is'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
and
replace 'and' by 'code'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
encoding
replace 'encoding' by 'obfuscating'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
between
replace 'between' by 'and'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
difference
replace 'difference' by 'encoding'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
main
replace 'main' by 'between'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
The
replace 'The' by 'difference'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
randomization.
replace 'randomization.' by 'main'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
[S'A
replace '[S'A' by 'The'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
of
replace 'of' by 'randomization.'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
simplification
replace 'simplification' by '[S'A'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
a
replace 'a' by 'of'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
as
replace 'as' by 'simplification'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
seen
replace 'seen' by 'a'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
be
replace 'be' by 'as'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
can
replace 'can' by 'seen'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
obfuscation
replace 'obfuscation' by 'be'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
Code
replace 'Code' by 'can'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
obfuscation:
replace 'obfuscation:' by 'obfuscation'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
insert '' by 'obfuscation:'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
insert '' by 'Code'
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the latter requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, the wobfuscator targets JavaScript code, not WebAssembly binaries.
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
[
Unpaired symbol: ‘]’ seems to be missing
Code obfuscation: Code obfuscation can be seen as a simplification of [S'A randomization. The main difference between encoding and obfuscating code is that the former requires the final target to know the encoding key while the latter executes as is in any client [97]. Yet, both strategies aim to tackle static reverse engineering of programs. In the context of WebAssembly, Romano et al. [87] proposed an obfuscation technique, wobfuscator, for JavaScript in which part of the code is replaced by calls to complementary WebAssembly functions. Yet, wobfuscator targets JavaScript code, not WebAssembly binaries.
insert '' by 'Checking:'
Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking
Equivalence Checking
insert '' by 'Equivalence'
Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking: Equivalence Checking
Equivalence Checking
deployment
replace 'deployment' by 'deployment.'
Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment.
Variants deployment
insert '' by 'deployment.'
Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment.
Variants deployment
insert '' by 'Variants'
Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment. Variants deployment.
Variants deployment
verification:
delete 'verification:' by ''
In the absence of a test suite or a technique that inherently implements the equivalence property, the works mentioned earlier use automated theorem provers. Theorem provers rely on SMT solvers [107] to prove the equivalence of program variants. The central idea for theorem provers is to convert the two code variants into mathematical formulas. The core component, the SMT solver, then checks for counter-examples that satisfy the negation of the mathematical formulas [108]. When it finds a counter-example, it uncovers an input for which the two mathematical formulas yield different outputs. The primary limitation of this technique resides in the conversion process. All algorithms can be translated into a mathematical formula. However, under certain theories such as loops for linear arithmetic, the satisfiability query may be undecidable. As a result, SMT solvers cannot make a decision. Nevertheless, this technique is frequently used for checking no-jump-programs like basic block and peephole replacements [109].
Formal verification: In the absence of a test suite or a technique that inherently implements the equivalence property, the works mentioned earlier use automated theorem provers. Theorem provers rely on SMT solvers [107] to prove the equivalence of program variants. The central idea for theorem provers is to convert the two code variants into mathematical formulas. The core component, the SMT solver, then checks for counter-examples that satisfy the negation of the mathematical formulas [108]. When it finds a counter-example, it uncovers an input for which the two mathematical formulas yield different outputs. The primary limitation of this technique resides in the conversion process. All algorithms can be translated into a mathematical formula. However, under certain theories such as loops for linear arithmetic, the satisfiability query may be undecidable. As a result, SMT solvers cannot make a decision. Nevertheless, this technique is frequently used for checking no-jump-programs like basic block and peephole replacements [109].
Formal
delete 'Formal' by ''
In the absence of a test suite or a technique that inherently implements the equivalence property, the works mentioned earlier use automated theorem provers. Theorem provers rely on SMT solvers [107] to prove the equivalence of program variants. The central idea for theorem provers is to convert the two code variants into mathematical formulas. The core component, the SMT solver, then checks for counter-examples that satisfy the negation of the mathematical formulas [108]. When it finds a counter-example, it uncovers an input for which the two mathematical formulas yield different outputs. The primary limitation of this technique resides in the conversion process. All algorithms can be translated into a mathematical formula. However, under certain theories such as loops for linear arithmetic, the satisfiability query may be undecidable. As a result, SMT solvers cannot make a decision. Nevertheless, this technique is frequently used for checking no-jump-programs like basic block and peephole replacements [109].
Formal verification: In the absence of a test suite or a technique that inherently implements the equivalence property, the works mentioned earlier use automated theorem provers. Theorem provers rely on SMT solvers [107] to prove the equivalence of program variants. The central idea for theorem provers is to convert the two code variants into mathematical formulas. The core component, the SMT solver, then checks for counter-examples that satisfy the negation of the mathematical formulas [108]. When it finds a counter-example, it uncovers an input for which the two mathematical formulas yield different outputs. The primary limitation of this technique resides in the conversion process. All algorithms can be translated into a mathematical formula. However, under certain theories such as loops for linear arithmetic, the satisfiability query may be undecidable. As a result, SMT solvers cannot make a decision. Nevertheless, this technique is frequently used for checking no-jump-programs like basic block and peephole replacements [109].
tests:
delete 'tests:' by ''
The process of checking modulo tests involves utilizing a test suite to confirm the equivalence of program variants [103, 104]. When a program variant successfully passes the test suite, it is deemed equivalent to the original. It is reasonable to assume that projects prioritizing quality and security are likely to have a robust test suite that facilitates this type of equivalence checking. However, this technique’s effectiveness is limited by the necessity for a preexisting test suite. Yet, as an alternative, fuzzers can be used to automatically generate tests [105]. Fuzzers operate by randomly generating inputs that lead to different observable behaviors. If a variant produces a different output from two identical inputs, it is not equivalent to the original program. Fuzzers’ primary drawback is their time-consuming nature and the requirement for manually introducing oracles. Recent advancements in the field of machine learning have led researchers to explore the application of neural networks in verifying program equivalence. Zhang and his team’s work provides an example of this, where Large Language Models are used to generate reference oracles and test cases [106]. Despite its effectiveness, this method attains an accuracy rate of just 88%, which falls short of providing complete verification.
Checking modulo tests: The process of checking modulo tests involves utilizing a test suite to confirm the equivalence of program variants [103, 104]. When a program variant successfully passes the test suite, it is deemed equivalent to the original. It is reasonable to assume that projects prioritizing quality and security are likely to have a robust test suite that facilitates this type of equivalence checking. However, this technique’s effectiveness is limited by the necessity for a preexisting test suite. Yet, as an alternative, fuzzers can be used to automatically generate tests [105]. Fuzzers operate by randomly generating inputs that lead to different observable behaviors. If a variant produces a different output from two identical inputs, it is not equivalent to the original program. Fuzzers’ primary drawback is their time-consuming nature and the requirement for manually introducing oracles. Recent advancements in the field of machine learning have led researchers to explore the application of neural networks in verifying program equivalence. Zhang and his team’s work provides an example of this, where Large Language Models are used to generate reference oracles and test cases [106]. Despite its effectiveness, this method attains an accuracy rate of just 88%, which falls short of providing complete verification.
modulo
delete 'modulo' by ''
The process of checking modulo tests involves utilizing a test suite to confirm the equivalence of program variants [103, 104]. When a program variant successfully passes the test suite, it is deemed equivalent to the original. It is reasonable to assume that projects prioritizing quality and security are likely to have a robust test suite that facilitates this type of equivalence checking. However, this technique’s effectiveness is limited by the necessity for a preexisting test suite. Yet, as an alternative, fuzzers can be used to automatically generate tests [105]. Fuzzers operate by randomly generating inputs that lead to different observable behaviors. If a variant produces a different output from two identical inputs, it is not equivalent to the original program. Fuzzers’ primary drawback is their time-consuming nature and the requirement for manually introducing oracles. Recent advancements in the field of machine learning have led researchers to explore the application of neural networks in verifying program equivalence. Zhang and his team’s work provides an example of this, where Large Language Models are used to generate reference oracles and test cases [106]. Despite its effectiveness, this method attains an accuracy rate of just 88%, which falls short of providing complete verification.
Checking modulo tests: The process of checking modulo tests involves utilizing a test suite to confirm the equivalence of program variants [103, 104]. When a program variant successfully passes the test suite, it is deemed equivalent to the original. It is reasonable to assume that projects prioritizing quality and security are likely to have a robust test suite that facilitates this type of equivalence checking. However, this technique’s effectiveness is limited by the necessity for a preexisting test suite. Yet, as an alternative, fuzzers can be used to automatically generate tests [105]. Fuzzers operate by randomly generating inputs that lead to different observable behaviors. If a variant produces a different output from two identical inputs, it is not equivalent to the original program. Fuzzers’ primary drawback is their time-consuming nature and the requirement for manually introducing oracles. Recent advancements in the field of machine learning have led researchers to explore the application of neural networks in verifying program equivalence. Zhang and his team’s work provides an example of this, where Large Language Models are used to generate reference oracles and test cases [106]. Despite its effectiveness, this method attains an accuracy rate of just 88%, which falls short of providing complete verification.
Checking
delete 'Checking' by ''
The process of checking modulo tests involves utilizing a test suite to confirm the equivalence of program variants [103, 104]. When a program variant successfully passes the test suite, it is deemed equivalent to the original. It is reasonable to assume that projects prioritizing quality and security are likely to have a robust test suite that facilitates this type of equivalence checking. However, this technique’s effectiveness is limited by the necessity for a preexisting test suite. Yet, as an alternative, fuzzers can be used to automatically generate tests [105]. Fuzzers operate by randomly generating inputs that lead to different observable behaviors. If a variant produces a different output from two identical inputs, it is not equivalent to the original program. Fuzzers’ primary drawback is their time-consuming nature and the requirement for manually introducing oracles. Recent advancements in the field of machine learning have led researchers to explore the application of neural networks in verifying program equivalence. Zhang and his team’s work provides an example of this, where Large Language Models are used to generate reference oracles and test cases [106]. Despite its effectiveness, this method attains an accuracy rate of just 88%, which falls short of providing complete verification.
Checking modulo tests: The process of checking modulo tests involves utilizing a test suite to confirm the equivalence of program variants [103, 104]. When a program variant successfully passes the test suite, it is deemed equivalent to the original. It is reasonable to assume that projects prioritizing quality and security are likely to have a robust test suite that facilitates this type of equivalence checking. However, this technique’s effectiveness is limited by the necessity for a preexisting test suite. Yet, as an alternative, fuzzers can be used to automatically generate tests [105]. Fuzzers operate by randomly generating inputs that lead to different observable behaviors. If a variant produces a different output from two identical inputs, it is not equivalent to the original program. Fuzzers’ primary drawback is their time-consuming nature and the requirement for manually introducing oracles. Recent advancements in the field of machine learning have led researchers to explore the application of neural networks in verifying program equivalence. Zhang and his team’s work provides an example of this, where Large Language Models are used to generate reference oracles and test cases [106]. Despite its effectiveness, this method attains an accuracy rate of just 88%, which falls short of providing complete verification.
propose
delete 'propose' by ''
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
al.
delete 'al.' by ''
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
et
delete 'et' by ''
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Aga
replace 'Aga' by 'Propose'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
combinations.
replace 'combinations.' by 'al.'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
flag
replace 'flag' by 'et'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
compiler
replace 'compiler' by 'Aga'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
different
replace 'different' by 'combinations.'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
using
replace 'using' by 'flag'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
by
replace 'by' by 'compiler'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
program
replace 'program' by 'different'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
a
replace 'a' by 'using'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
of
replace 'of' by 'by'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
variants
replace 'variants' by 'program'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
multiple
replace 'multiple' by 'a'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
generates
replace 'generates' by 'of'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Cornucopia
replace 'Cornucopia' by 'variants'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
[111].
replace '[111].' by 'multiple'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Cornucopia
replace 'Cornucopia' by 'generates'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
propose
replace 'propose' by 'Cornucopia'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
al.,
replace 'al.,' by '[111].'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
et
replace 'et' by 'Cornucopia'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Singhal
replace 'Singhal' by 'propose'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Similarly,
replace 'Similarly,' by 'al.'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
multistacks.
replace 'multistacks.' by 'et'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
termed
replace 'termed' by 'Singhal'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
parts,
replace 'parts,' by 'Similarly,'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
component
replace 'component' by 'multistacks.'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
several
replace 'several' by 'termed'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
into
replace 'into' by 'parts,'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
stack
replace 'stack' by 'component'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
conventional
replace 'conventional' by 'several'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
a
replace 'a' by 'into'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
partition
replace 'partition' by 'stack'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
to
replace 'to' by 'conventional'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
4.1
replace '4.1' by 'a'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
GCC
replace 'GCC' by 'partition'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
of
replace 'of' by 'to'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
version
replace 'version' by '4.1'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
a
replace 'a' by 'GCC'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
adapt
replace 'adapt' by 'of'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
They
replace 'They' by 'version'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
process.
replace 'process.' by 'a'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
compilation
replace 'compilation' by 'adapt'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
the
replace 'the' by 'They'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
from
replace 'from' by 'process.'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
binaries
replace 'binaries' by 'compilation'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
varying
replace 'varying' by 'the'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
generate
replace 'generate' by 'from'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
to
replace 'to' by 'binaries'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
compiler
replace 'compiler' by 'varying'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
custom
replace 'custom' by 'generate'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
a
replace 'a' by 'to'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
employing
replace 'employing' by 'compiler'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
suggest
replace 'suggest' by 'custom'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
[110]
replace '[110]' by 'a'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
al.
replace 'al.' by 'employing'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
et
replace 'et' by 'suggest'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
El-Khalil
replace 'El-Khalil' by '[110]'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
community.
replace 'community.' by 'al.'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
client
replace 'client' by 'et'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
the
replace 'the' by 'El-Khalil'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
of
replace 'of' by 'community.'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
segment
replace 'segment' by 'client'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
a
replace 'a' by 'the'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
affect
replace 'affect' by 'of'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
only
replace 'only' by 'segment'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
can
replace 'can' by 'a'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
binaries
replace 'binaries' by 'affect'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
vulnerable
replace 'vulnerable' by 'only'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
as
replace 'as' by 'can'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
immunity,
replace 'immunity,' by 'binaries'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
herd
replace 'herd' by 'vulnerable'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization
delete 'Randomization' by ''
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
in
replace 'in' by 'immunity,'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
pool
replace 'pool' by 'herd'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
variant
replace 'variant' by 'as'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
the
replace 'the' by 'Randomization'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
[73]
replace '[73]' by 'pool'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
al.
replace 'al.' by 'variant'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
et
replace 'et' by 'the'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Jackson
replace 'Jackson' by 'define'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
deployment.
replace 'deployment.' by '[73]'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
each
replace 'each' by 'al.'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
during
replace 'during' by 'et'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
client
replace 'client' by 'Jackson'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
the
replace 'the' by 'deployment.'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
a
replace 'a' by 'each'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
to
replace 'to' by 'during'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
assigned
replace 'assigned' by 'client'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
is
replace 'is' by 'a'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
pool),
replace 'pool),' by 'to'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
variant
replace 'variant' by 'assigned'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
program’s
replace 'program’s' by 'is'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
the
replace 'the' by 'pool),'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
as
replace 'as' by 'variant'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
to
replace 'to' by 'program’s'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
(referred
replace '(referred' by 'the'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
variants
replace 'variants' by 'as'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
collection
replace 'collection' by '(referred'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
a
replace 'a' by 'variants'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
from
replace 'from' by 'of'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
chosen
replace 'chosen' by 'collection'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
randomly
replace 'randomly' by 'a'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
program,
replace 'program,' by 'from'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
a
replace 'a' by 'selected'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
setup,
replace 'setup,' by 'randomly'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
this
replace 'this' by 'program,'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
In
replace 'In' by 'a'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
clients.
replace 'clients.' by 'setup,'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
different
replace 'different' by 'this'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
to
replace 'to' by 'In'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
variants
replace 'variants' by 'clients.'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
present
delete 'present' by ''
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
ability
delete 'ability' by ''
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
program’s
replace 'program’s' by 'variants'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
a
replace 'a' by 'different'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
denotes
replace 'denotes' by 'present'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization
replace 'Randomization' by 'to'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
term
replace 'term' by 'ability'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
the
replace 'the' by 'program’s'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
work,
replace 'work,' by 'a'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
our
replace 'our' by 'denotes'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
of
replace 'of' by 'Randomization'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
context
replace 'context' by 'term'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
insert '' by 'work,'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
insert '' by 'our'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
insert '' by 'of'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
insert '' by 'context'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
In
replace 'In' by 'the'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization:
replace 'Randomization:' by 'In'
In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly selected from a collection of variants (referred to as the program’s variant pool), is assigned to a client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al. propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. Propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
Randomization: In the context of our work, the term Randomization denotes a program’s ability to present different variants to different clients. In this setup, a program, randomly chosen from a collection of variants (referred to as the program’s variant pool), is assigned to a the client during each deployment. Jackson et al. [73] define the variant pool in Randomization as herd immunity, as vulnerable binaries can only affect a segment of the client community. El-Khalil et al. [110] suggest employing a custom compiler to generate varying binaries from the compilation process. They adapt a version of GCC 4.1 to partition a conventional stack into several component parts, termed multistacks. Similarly, Singhal et al., propose Cornucopia [111]. Cornucopia generates multiple variants of a program by using different compiler flag combinations. Aga et al. propose the generation of program variants through the randomization of its data layout in memory [88]. This method allows each variant to operate on the same data in memory but at different memory offsets. Randomization can also be applied to virtual machines and operating systems. On this note, Kc et al. [92] establish a unique mapping between artificial CPU instructions and actual ones, enabling the assignment of various variants to specific target clients. In a similar vein, Xu et al. [91] recompile the Linux Kernel to minimize the exposure time of persistent memory objects, thereby increasing the frequency of address randomization.
propose
delete 'propose' by ''
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
al.
delete 'al.' by ''
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
et
delete 'et' by ''
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Davi
delete 'Davi' by ''
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
[114].
delete '[114].' by ''
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
inconsistencies
replace 'inconsistencies' by 'Propose'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
memory
replace 'memory' by 'al.'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
of
replace 'of' by 'et'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
detection
replace 'detection' by 'Davi'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
the
replace 'the' by '[114].'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
for
replace 'for' by 'inconsistencies'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
allowing
replace 'allowing' by 'memory'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
direction,
replace 'direction,' by 'of'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
opposite
replace 'opposite' by 'detection'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
in
replace 'in' by 'for'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
grows
replace 'grows' by 'allowing'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
that
replace 'that' by 'direction,'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
stack
replace 'stack' by 'opposite'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
a
replace 'a' by 'the'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
have
replace 'have' by 'in'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
variants
replace 'variants' by 'grows'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
These
replace 'These' by 'that'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
variants.
replace 'variants.' by 'stack'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Intel
replace 'Intel' by 'a'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
32-bit
replace '32-bit' by 'have'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
generate
replace 'generate' by 'variants'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
to
replace 'to' by 'These'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
library
replace 'library' by 'variants.'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
standard
replace 'standard' by 'Intel'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
a
replace 'a' by '32-bit'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
modify
replace 'modify' by 'generate'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
al.
replace 'al.' by 'to'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
et
replace 'et' by 'library'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Salamat
replace 'Salamat' by 'standard'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
time,
replace 'time,' by 'a'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
same
replace 'same' by 'Modify'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
the
replace 'the' by 'al.'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
At
replace 'At' by 'et'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
[113].
replace '[113].' by 'Salamat'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
layouts
replace 'layouts' by 'time,'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
memory
replace 'memory' by 'same'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
randomized
replace 'randomized' by 'the'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
and
replace 'and' by 'At'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
non-overlapping
replace 'non-overlapping' by '[113].'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
introducing
replace 'introducing' by 'layouts'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
parallel,
replace 'parallel,' by 'memory'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
in
replace 'in' by 'randomized'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
variants
replace 'variants' by 'and'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
two
replace 'two' by 'non-overlapping'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
executing
replace 'executing' by 'introducing'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
of
replace 'of' by 'parallel,'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
concept
replace 'concept' by 'in'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
the
replace 'the' by 'variants'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
extend
replace 'extend' by 'two'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
al.
replace 'al.' by 'executing'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
et
replace 'et' by 'of'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Bruschi
replace 'Bruschi' by 'concept'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
[83].
replace '[83].' by 'the'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
paths
replace 'paths' by 'Extended'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
execution
replace 'execution' by 'al.'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
randomize
replace 'randomize' by 'et'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
to
replace 'to' by 'Bruschi'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
program
replace 'program' by '[83].'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
single
replace 'single' by 'paths'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
a
replace 'a' by 'execution'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
as
replace 'as' by 'randomize'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
or
replace 'or' by 'to'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
inconsistencies,
replace 'inconsistencies,' by 'program'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
for
replace 'for' by 'single'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
check
replace 'check' by 'a'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
to
replace 'to' by 'as'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
parallel
replace 'parallel' by 'or'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
in
replace 'in' by 'inconsistencies,'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
execute
replace 'execute' by 'for'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
either
replace 'either' by 'check'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
can
replace 'can' by 'to'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
variants
replace 'variants' by 'parallel'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
embedded
replace 'embedded' by 'in'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
These
replace 'These' by 'execute'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
runtime.
replace 'runtime.' by 'either'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
at
replace 'at' by 'can'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
program
delete 'program' by ''
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
insert '' by 'These'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
insert '' by 'runtime.'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
its
replace 'its' by 'at'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
executes
replace 'executes' by 'variants'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
binary
replace 'binary' by 'program'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
the
replace 'the' by 'its'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Then,
replace 'Then,' by 'executes'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
client.
replace 'client.' by 'binary'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
a
replace 'a' by 'multivariant'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
to
replace 'to' by 'the'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
deployed
replace 'deployed' by 'Then,'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
randomly
replace 'randomly' by 'client.'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
is
replace 'is' by 'a'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
binary
replace 'binary' by 'to'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Each
replace 'Each' by 'randomly'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
[112].
replace '[112].' by 'is'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
a
delete 'a' by ''
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
as
replace 'as' by 'Each'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
known
replace 'known' by '[112].'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
binary,
replace 'binary,' by 'binary'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
single
replace 'single' by 'multivariant'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
insert '' by 'as'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
insert '' by 'known'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
into
replace 'into' by 'binary,'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
composed
replace 'composed' by 'single'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
are
replace 'are' by 'a'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
variants
replace 'variants' by 'into'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
program
replace 'program' by 'composed'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multiple
replace 'Multiple' by 'are'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
(MVE):
replace '(MVE):' by 'variants'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Execution
replace 'Execution' by 'program'
Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. Extended the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. Modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. Propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Multivariant Execution (MVE): Multiple program variants are composed into a single binary, known as a multivariant binary [112]. Each multivariant binary is randomly deployed to a client. Then, the multivariant binary executes its embedded program variants at runtime. These embedded variants can either execute in parallel to check for inconsistencies, or as a single program to randomize execution paths [83]. Bruschi et al. extend the concept of executing two variants in parallel, introducing non-overlapping and randomized memory layouts [113]. At the same time, Salamat et al. modify a standard library to generate 32-bit Intel variants. These variants have a stack that grows in the opposite direction, allowing for the detection of memory inconsistencies [114]. Davi et al. propose Isomeron, an approach for execution-path randomization [115]. Isomeron operates by simultaneously loading the original program and a variant. It then uses a coin flip to determine which copy of the program to execute next at the function call level. Previous works have highlighted the benefits of limiting execution to only two variants in a multivariant environment. Agosta et al., as well as Crane et al., used more than two generated programs in the multivariant composition, thereby randomizing software control flow at runtime [116, 86]. Both strategies have proven effective in enhancing security by addressing known vulnerabilities, such as Just-In-Time Return-Oriented Programming (JIT-ROP) attacks [117] and power side-channel attacks [118]. Lastly, only Voulimeneas et al. [119] have recently proposed a multivariant execution system that enhances security by parallelizing the execution of variants across different machines.
Diversification
replace 'Diversification' by 'Diversification.'
Measuring Software Diversification.
Measuring Software Diversification
that,
replace 'that,' by 'that'
Dynamic comparison of variants: Static comparisons between variants inherently have limitations. For example, two variants may show differences at the source code level but exhibit identical behavior during execution. Take the addition of nop operations to a program as an example. Despite source code level differences, the variant and the original program execute identical instructions, leading to similar behaviors modulo input. Measuring Software Diversification primarily aims to demonstrate variant-specific observabilities. While static differences are usually observable, runtime information holds complementary relevance [125]. Therefore, dynamic metrics are essential to assess the diversity of variants. For instance, Forrest et al. [126] were pioneers in classifying program behaviors by analyzing their system call traces using n-grams profiling. Cabrera et al. Use a global alignments approach to gauge the diversity of JavaScript bytecode traces within the Chrome browser [14]. Fang et al. proposed a method to counteract JavaScript obfuscation techniques used in malicious code, by analyzing dynamic information captured from V8 bytecode traces [127]. Dynamic metrics are primarily employed to cluster similar behaviors. Following the same logic, the diversity is greater when the difference between behaviors is larger. Notice that dynamic metrics can be difficult due to the expense of program execution or the complication of required user interaction. On the other hand, malware programs, which usually do not require user interaction, are simpler to evaluate in controlled environments before actual deployment.
Dynamic comparison of variants: Static comparisons between variants inherently have limitations. For example, two variants may show differences at the source code level but exhibit identical behavior during execution. Take the addition of nop operations to a program as an instance. Despite source code level differences, the variant and the original program execute identical instructions, leading to similar behaviors modulo input. Measuring Software Diversification primarily aims to demonstrate variant-specific observabilities. While static differences are usually observable, runtime information holds complementary relevance [125]. Therefore, dynamic metrics are essential to assess the diversity of variants. For instance, Forrest et al. [126] were pioneers in classifying program behaviors by analyzing their system call traces using n-grams profiling. Cabrera et al. used a global alignments approach to gauge the diversity of JavaScript bytecode traces within the Chrome browser [14]. Fang et al. proposed a method to counteract JavaScript obfuscation techniques used in malicious code, by analyzing dynamic information captured from V8 bytecode traces [127]. Dynamic metrics are primarily employed to cluster similar behaviors. Following the same logic, the diversity is greater when the difference between behaviors is larger. Notice that, dynamic metrics can be difficult due to the expense of program execution or the complication of required user interaction. On the other hand, malware programs, which usually do not require user interaction, are simpler to evaluate in controlled environments before actual deployment.
used
replace 'used' by 'Use'
Dynamic comparison of variants: Static comparisons between variants inherently have limitations. For example, two variants may show differences at the source code level but exhibit identical behavior during execution. Take the addition of nop operations to a program as an example. Despite source code level differences, the variant and the original program execute identical instructions, leading to similar behaviors modulo input. Measuring Software Diversification primarily aims to demonstrate variant-specific observabilities. While static differences are usually observable, runtime information holds complementary relevance [125]. Therefore, dynamic metrics are essential to assess the diversity of variants. For instance, Forrest et al. [126] were pioneers in classifying program behaviors by analyzing their system call traces using n-grams profiling. Cabrera et al. Use a global alignments approach to gauge the diversity of JavaScript bytecode traces within the Chrome browser [14]. Fang et al. proposed a method to counteract JavaScript obfuscation techniques used in malicious code, by analyzing dynamic information captured from V8 bytecode traces [127]. Dynamic metrics are primarily employed to cluster similar behaviors. Following the same logic, the diversity is greater when the difference between behaviors is larger. Notice that dynamic metrics can be difficult due to the expense of program execution or the complication of required user interaction. On the other hand, malware programs, which usually do not require user interaction, are simpler to evaluate in controlled environments before actual deployment.
Dynamic comparison of variants: Static comparisons between variants inherently have limitations. For example, two variants may show differences at the source code level but exhibit identical behavior during execution. Take the addition of nop operations to a program as an instance. Despite source code level differences, the variant and the original program execute identical instructions, leading to similar behaviors modulo input. Measuring Software Diversification primarily aims to demonstrate variant-specific observabilities. While static differences are usually observable, runtime information holds complementary relevance [125]. Therefore, dynamic metrics are essential to assess the diversity of variants. For instance, Forrest et al. [126] were pioneers in classifying program behaviors by analyzing their system call traces using n-grams profiling. Cabrera et al. used a global alignments approach to gauge the diversity of JavaScript bytecode traces within the Chrome browser [14]. Fang et al. proposed a method to counteract JavaScript obfuscation techniques used in malicious code, by analyzing dynamic information captured from V8 bytecode traces [127]. Dynamic metrics are primarily employed to cluster similar behaviors. Following the same logic, the diversity is greater when the difference between behaviors is larger. Notice that, dynamic metrics can be difficult due to the expense of program execution or the complication of required user interaction. On the other hand, malware programs, which usually do not require user interaction, are simpler to evaluate in controlled environments before actual deployment.
instance.
replace 'instance.' by 'example.'
Dynamic comparison of variants: Static comparisons between variants inherently have limitations. For example, two variants may show differences at the source code level but exhibit identical behavior during execution. Take the addition of nop operations to a program as an example. Despite source code level differences, the variant and the original program execute identical instructions, leading to similar behaviors modulo input. Measuring Software Diversification primarily aims to demonstrate variant-specific observabilities. While static differences are usually observable, runtime information holds complementary relevance [125]. Therefore, dynamic metrics are essential to assess the diversity of variants. For instance, Forrest et al. [126] were pioneers in classifying program behaviors by analyzing their system call traces using n-grams profiling. Cabrera et al. Use a global alignments approach to gauge the diversity of JavaScript bytecode traces within the Chrome browser [14]. Fang et al. proposed a method to counteract JavaScript obfuscation techniques used in malicious code, by analyzing dynamic information captured from V8 bytecode traces [127]. Dynamic metrics are primarily employed to cluster similar behaviors. Following the same logic, the diversity is greater when the difference between behaviors is larger. Notice that dynamic metrics can be difficult due to the expense of program execution or the complication of required user interaction. On the other hand, malware programs, which usually do not require user interaction, are simpler to evaluate in controlled environments before actual deployment.
Dynamic comparison of variants: Static comparisons between variants inherently have limitations. For example, two variants may show differences at the source code level but exhibit identical behavior during execution. Take the addition of nop operations to a program as an instance. Despite source code level differences, the variant and the original program execute identical instructions, leading to similar behaviors modulo input. Measuring Software Diversification primarily aims to demonstrate variant-specific observabilities. While static differences are usually observable, runtime information holds complementary relevance [125]. Therefore, dynamic metrics are essential to assess the diversity of variants. For instance, Forrest et al. [126] were pioneers in classifying program behaviors by analyzing their system call traces using n-grams profiling. Cabrera et al. used a global alignments approach to gauge the diversity of JavaScript bytecode traces within the Chrome browser [14]. Fang et al. proposed a method to counteract JavaScript obfuscation techniques used in malicious code, by analyzing dynamic information captured from V8 bytecode traces [127]. Dynamic metrics are primarily employed to cluster similar behaviors. Following the same logic, the diversity is greater when the difference between behaviors is larger. Notice that, dynamic metrics can be difficult due to the expense of program execution or the complication of required user interaction. On the other hand, malware programs, which usually do not require user interaction, are simpler to evaluate in controlled environments before actual deployment.
exist
replace 'exist' by 'are'
In the context of WebAssembly, there are no explicit works on Software Diversification. Consequently, previous metrics have not been directly applied to measure diversification in WebAssembly binaries. However, in other domains, such as the analysis of WebAssembly binaries, several studies have employed static metrics. For example, VeriWasm quantifies attack-based patterns, stating that a WebAssembly binary is more secure with a lower pattern count [128]. This metric might potentially serve as a guide during variant generation. In the field of malware detection, MINOS [28] proposes transforming WebAssembly binaries into grayscale images. They then employ convolutional neural networks to identify malware, where an increased similarity to a malware image increases the probability of the binary being malware. Regarding the dynamic comparisons, Wang et al.’s study [29] profiles WebAssembly instructions during runtime to identify malicious behavior.
In the context of WebAssembly, there exist no explicit works on Software Diversification. Consequently, previous metrics have not been directly applied to measure diversification in WebAssembly binaries. However, in other domains, such as the analysis of WebAssembly binaries, several studies have employed static metrics. For example, VeriWasm quantifies attack-based patterns, stating that a WebAssembly binary is more secure with a lower pattern count [128]. This metric might potentially serve as a guide during variant generation. In the field of malware detection, MINOS [28] proposes transforming WebAssembly binaries into grayscale images. They then employ convolutional neural networks to identify malware, where an increased similarity to a malware image increases the probability of the binary being malware. Regarding the dynamic comparisons, Wang et al.’s study [29] profiles WebAssembly instructions during runtime to identify malicious behavior.
propose
replace 'propose' by 'Propose'
Malicious actors may employ previously discussed diversification strategies to evade detection [131]. For instance, in the Web context, Weihang et al. Propose to randomly transform HTML elements of web pages to evade advertisement blockers [132]. Over time, evasion techniques have evolved in both complexity and sophistication [133]. Chua et al. [134], for instance, suggested a framework for automatically obfuscating the source code of Android applications using method overloading, opaque predicates, try-catch, and switch statement obfuscation, resulting in multiple versions of identical malware. Moreover, machine learning approaches have been used to develop evasive malware [135], drawing on a corpus of pre-existing malware [124]. These methods aim to thwart static malware detectors, yet, more advanced techniques focus on evading dynamic detection mostly by employing throttling [136, 137].
Malicious actors may employ previously discussed diversification strategies to evade detection [131]. For instance, in the Web context, Weihang et al. propose to randomly transform HTML elements of web pages to evade advertisement blockers [132]. Over time, evasion techniques have evolved in both complexity and sophistication [133]. Chua et al. [134], for instance, suggested a framework for automatically obfuscating the source code of Android applications using method overloading, opaque predicates, try-catch, and switch statement obfuscation, resulting in multiple versions of identical malware. Moreover, machine learning approaches have been used to develop evasive malware [135], drawing on a corpus of pre-existing malware [124]. These methods aim to thwart static malware detectors, yet, more advanced techniques focus on evading dynamic detection mostly by employing throttling [136, 137].
Diversification:
delete 'Diversification:' by ''
Offensive Diversification is conceptually equal to Defensive Software Diversification. Yet, in an offensive context, one may apply diversification techniques to malware or other malicious codes to evade detection by security software [130]. One might equate Offensive Diversification with Code obfuscation, if its purpose shifts from preventing reverse engineering by malicious actors, to evading detection by malware analysis systems.
Offensive Diversification: Offensive Diversification is conceptually equal to Defensive Software Diversification. Yet, in an offensive context, one may apply diversification techniques to malware or other malicious codes to evade detection by security software [130]. One might equate Offensive Diversification with Code obfuscation, if its purpose shifts from preventing reverse engineering by malicious actors, to evading detection by malware analysis systems.
Offensive
delete 'Offensive' by ''
Offensive Diversification is conceptually equal to Defensive Software Diversification. Yet, in an offensive context, one may apply diversification techniques to malware or other malicious codes to evade detection by security software [130]. One might equate Offensive Diversification with Code obfuscation, if its purpose shifts from preventing reverse engineering by malicious actors, to evading detection by malware analysis systems.
Offensive Diversification: Offensive Diversification is conceptually equal to Defensive Software Diversification. Yet, in an offensive context, one may apply diversification techniques to malware or other malicious codes to evade detection by security software [130]. One might equate Offensive Diversification with Code obfuscation, if its purpose shifts from preventing reverse engineering by malicious actors, to evading detection by malware analysis systems.
Diversification
delete 'Diversification' by ''
Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Software
replace 'Software' by 'diversification'
Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Defensive
replace 'Defensive' by 'software'
Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
hand,
replace 'hand,' by 'defensive'
Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
one
replace 'one' by 'hand,'
Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
the
replace 'the' by 'one'
Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
On
replace 'On' by 'the'
Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Diversification.
replace 'Diversification.' by 'On'
Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Offensive
replace 'Offensive' by 'offensive.'
Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Defensive
replace 'Defensive' by 'defensive'
Lundquist et al. [75] distinguish Software Diversification into two categories: defensive and offensive. On the one hand, defensive software diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
Lundquist et al. [75] distinguish Software Diversification into two categories: Defensive and Offensive Diversification. On the one hand, Defensive Software Diversification introduces unpredictability in system behavior. By making software less predictable, defensive Software Diversification aims to proactively deter attacks, acting as a complementary strategy to other, more reactive, security measures. The majority of previously discussed works in this section contribute to defensive diversification. Yet, Software Diversification that aims to create diverse harmful programs is considered Offensive Diversification [129].
diversification
replace 'diversification' by 'diversification.'
Offensive or defensive assessment of diversification.
Offensive or Defensive assessment of diversification
Defensive
replace 'Defensive' by 'defensive'
Offensive or defensive assessment of diversification.
Offensive or Defensive assessment of diversification
Diversification
replace 'Diversification' by 'Diversification.'
Open challenges for Software Diversification.
Open challenges for Software Diversification
«¢
delete '«¢' by ''
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‘
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1
delete '1' by ''
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'
delete ''' by ''
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j
delete 'j' by ''
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i
delete 'i' by ''
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based
delete 'based' by ''
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fooo |. ) Wasm > , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢
|
delete '|' by ''
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fooo |. ) Wasm > , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢
Compiler
delete 'Compiler' by ''
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fooo |. ) Wasm > , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢
°
delete '°' by ''
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fooo |. ) Wasm > , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢
{
delete '{' by ''
Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re
fooo |. ) Wasm > , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢
>
replace '>' by '/>'
Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re
fooo |. ) Wasm > , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢
fooo
replace 'fooo' by 'Foo'
Foo |. ) Wasm /> , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re
fooo |. ) Wasm > , 1 _»-—+t i 1 ! ' i Wasm Source Frontend 1 Optimizer/ ' Code ‘Transformer ! ‘ { ee i a Backend i wasm-mutate ' toa ' wwe i Se nay — , 1 f brow Binary Saawess ie ae eaeaee ot re ° based ' 1 ' ' | Compiler ' | based i Multivariant j ' Wasm j ' 1 ‘ 1 ‘ MEWE «¢
insert '' by 'code.'
Source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. The process of generating WebAssembly binaries starts with the original source code.
source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. i process of generating WebAssembly binaries starts with the original
insert '' by 'source'
Source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. The process of generating WebAssembly binaries starts with the original source code.
source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. i process of generating WebAssembly binaries starts with the original
i
replace 'i' by 'The'
Source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. The process of generating WebAssembly binaries starts with the original source code.
source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. i process of generating WebAssembly binaries starts with the original
source
replace 'source' by 'Source'
Source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. The process of generating WebAssembly binaries starts with the original source code.
source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. i process of generating WebAssembly binaries starts with the original
source
This sentence does not start with an uppercase letter.
source code, which is then processed by a compiler to produce a WebAssembly binary. This compiler is generally divided into three main components [139]: a frontend that converts the source code into an intermediate representation, a transformer that modifies this representation usually for performance, and a backend that compiles the final WebAssembly binary. This architecture is illustrated in the leftmost part of Figure 3.1. i process of generating WebAssembly binaries starts with the original
Wheeler
replace 'Wheeler' by 'Wheeler.'
— David Wheeler.
— David Wheeler
FOR
delete 'FOR' by ''
AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERS
AUTOMATIC SOFTWARE DIVERSIFICATION FOR
DIVERSIFICATION
replace 'DIVERSIFICATION' by 'DIVERS'
AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERS
AUTOMATIC SOFTWARE DIVERSIFICATION FOR
insert '' by 'FOR'
AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERS
AUTOMATIC SOFTWARE DIVERSIFICATION FOR
insert '' by 'DIVERSIFICATION'
AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERS
AUTOMATIC SOFTWARE DIVERSIFICATION FOR
insert '' by 'SOFTWARE'
AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERS
AUTOMATIC SOFTWARE DIVERSIFICATION FOR
insert '' by 'AUTOMATIC'
AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERSIFICATION FOR AUTOMATIC SOFTWARE DIVERS
AUTOMATIC SOFTWARE DIVERSIFICATION FOR
synthesis
replace 'synthesis' by 'synthesis.'
Enumerative synthesis.
Enumerative synthesis
insert '' by 'Functions'
Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions
Function
insert '' by 'Functions:'
Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions
Function
Function
replace 'Function' by 'Functions:'
Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions: Functions
Function
transformations
replace 'transformations' by 'transformations.'
CROW is carefully designed to boost the generation of variants as much as possible. First, we disable the majority of the pruning strategies. Instead of preventing the generation of commutative operations during the search, CROW still uses such transformation as a strategy to generate program variants. Second, CROW applies code transformations independently. For instance, if a suitable replacement is identified that can be applied at N different locations in the original program, CROW will generate 2" distinct program variants, i.e., the power set of applying the transformation or not to each location. This approach leads to a combinatorial explosion in the number of available program variants, especially as the number of possible replacements increases. Third, we remove all built-in optimizations in the LLVM backend that could reverse Wasm variants, i.e., we disable all optimizations in the Wasm backend that could reverse the CROW transformations.
CROW is carefully designed to boost the generation of variants as much as possible. First, we disable the majority of the pruning strategies. Instead of preventing the generation of commutative operations during the search, CROW still uses such transformation as a strategy to generate program variants. Second, CROW applies code transformations independently. For instance, if a suitable replacement is identified that can be applied at N different locations in the original program, CROW will generate 2" distinct program variants, i.e., the power set of applying the transformation or not to each location. This approach leads to a combinatorial explosion in the number of available program variants, especially as the number of possible replacements increases. Third, we remove all built-in optimizations in the LLVM backend that could reverse Wasm variants, i.e., we disable all optimizations in the Wasm backend that could reverse the CROW transformations
{
delete '{' by ''
Int babbage() int current = 25264;
int babbage() { int current = 25264;
int
replace 'int' by 'Int'
Int babbage() int current = 25264;
int babbage() { int current = 25264;
;}
delete ';}' by ''
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
0
delete '0' by ''
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
3
delete '3' by ''
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
>
delete '>' by ''
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
current)
replace 'current)' by ';'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
%d\n",
replace '%d\n",' by '0'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
is
replace 'is' by 'return'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
number
replace 'number' by '3'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
("The
replace '("The' by '>'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
printf
replace 'printf' by 'current)'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
25264;
replace '25264;' by '%dn",'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
=
replace '=' by 'is'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
current
replace 'current' by 'number'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
int
replace 'int' by '("The'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
{
replace '{' by 'printf'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
babbage()
replace 'babbage()' by '25264;'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
int
replace 'int' by '='
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
[40].
replace '[40].' by 'current'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
from
replace 'from' by 'int'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
Taken
replace 'Taken' by 'babbage()'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
problem.
replace 'problem.' by 'Int'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
Babbage
replace 'Babbage' by '[40].'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
3.1:
replace '3.1:' by 'from'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
Listing
replace 'Listing' by 'Taken'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
;
replace ';' by 'problem.'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
0
replace '0' by 'Babbage'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
current);
replace 'current);' by 'Listing'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
<
replace '<' by '0;'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
7%d\n",
replace '7%d\n",' by 'return'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
is
replace 'is' by 'current);'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
number
replace 'number' by '7%dn",'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
("The
replace '("The' by 'is'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
printf
replace 'printf' by 'number'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
+
replace '+' by '("The'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
current++;
replace 'current++;' by 'printf'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
{
replace '{' by '+'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
269696)
replace '269696)' by 'current++;'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
!=
replace '!=' by '269696)'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
1000000
replace '1000000' by '!='
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
<—
replace '<—' by '1000000'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
%
replace '%' by '—'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
((square=current*current)
replace '((square=current*current)' by '%'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
while
replace 'while' by '((square=current*current)'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
square;
replace 'square;' by 'while'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
0,
replace '0,' by 'square;'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
=
replace '=' by '0,'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
current
replace 'current' by '='
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
int
replace 'int' by 'current'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
{}
replace '{}' by 'int'
RwWNe NO int babbage() int current = 0, square; while ((square=current*current) % — 1000000 != 269696) current++; + printf ("The number is 7%dn", current); return 0; Listing 3.1: Babbage problem. Taken from [40]. Int babbage() int current = 25264; printf ("The number is %dn", current) > 3 return 0 ; Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
RwWNe NO int babbage() {} int current = 0, square; while ((square=current*current) % <— 1000000 != 269696) { current++; + printf ("The number is 7%d\n", < current); return 0 ; Listing 3.1: Babbage problem. Taken from [40]. int babbage() { int current = 25264; printf ("The number is %d\n", current) > 3 return 0 ;} Listing 3.2: Constant inferring transformation over the original Babbage problem in Listing 3.1. Taken from [40].
inferring
replace 'inferring' by 'inferring.'
Constant inferring.
Constant inferring
insert '' by 'NO'
NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO
NO
)
Unpaired symbol: ‘(’ seems to be missing
1 2 3 ing £Cint =) 4 return 2 * x + x; a Listing 3.3: C function that calculates the quantity 2x + x.
runtime?.
replace 'runtime?.' by 'runtime?'
CROW deals with this case, generating the program in Listing 3.2. It infers the value of current in Line 2 such that the Babbage condition is reached! Therefore, the condition in the loop will always be false. Then, the loop is dead code and is removed in the final compilation. The new program in Listing 3.2 is remarkably smaller and faster than the original code. Therefore, it offers differences both statically and at runtime?
CROW deals with this case, generating the program in Listing 3.2. It infers the value of current in Line 2 such that the Babbage condition is reached!. Therefore, the condition in the loop will always be false. Then, the loop is dead code and is removed in the final compilation. The new program in Listing 3.2 is remarkably smaller and faster than the original code. Therefore, it offers differences both statically and at runtime?.
reached!.
replace 'reached!.' by 'reached!'
CROW deals with this case, generating the program in Listing 3.2. It infers the value of current in Line 2 such that the Babbage condition is reached! Therefore, the condition in the loop will always be false. Then, the loop is dead code and is removed in the final compilation. The new program in Listing 3.2 is remarkably smaller and faster than the original code. Therefore, it offers differences both statically and at runtime?
CROW deals with this case, generating the program in Listing 3.2. It infers the value of current in Line 2 such that the Babbage condition is reached!. Therefore, the condition in the loop will always be false. Then, the loop is dead code and is removed in the final compilation. The new program in Listing 3.2 is remarkably smaller and faster than the original code. Therefore, it offers differences both statically and at runtime?.
?Notice
replace '?Notice' by 'Note'
In theory, this value can also be inferred by unrolling the loop the correct number of times with the LLVM toolchain. However, standard LLVM tools cannot unroll the while-loop because the loop count is too large. Note that for the sake of illustration, we show both codes in the C language; this process inside CROW is performed directly in LLVM IR.
lIn theory, this value can also be inferred by unrolling the loop the correct number of times with the LLVM toolchain. However, standard LLVM tools cannot unroll the while-loop because the loop count is too large. ?Notice that for the sake of illustration, we show both codes in the C language; this process inside CROW is performed directly in LLVM IR.
lIn
replace 'lIn' by 'In'
In theory, this value can also be inferred by unrolling the loop the correct number of times with the LLVM toolchain. However, standard LLVM tools cannot unroll the while-loop because the loop count is too large. Note that for the sake of illustration, we show both codes in the C language; this process inside CROW is performed directly in LLVM IR.
lIn theory, this value can also be inferred by unrolling the loop the correct number of times with the LLVM toolchain. However, standard LLVM tools cannot unroll the while-loop because the loop count is too large. ?Notice that for the sake of illustration, we show both codes in the C language; this process inside CROW is performed directly in LLVM IR.
Notice
Add a space between sentences.
lIn theory, this value can also be inferred by unrolling the loop the correct number of times with the LLVM toolchain. However, standard LLVM tools cannot unroll the while-loop because the loop count is too large. ?Notice that for the sake of illustration, we show both codes in the C language; this process inside CROW is performed directly in LLVM IR.
132.mul
replace '132.mul' by '132.mules.'
func $f (param i32) (result i32) local.get 0 1i32.const 3 132.mules.
func $f (param i32) (result i32) local.get 0 1i32.const 3 132.mul
get
If a new sentence starts here, add a space and start with an uppercase letter.
func $f (param i32) (result i32) local.get 0 1i32.const 3 132.mul
i32.add
replace 'i32.add' by 'i32'
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add
1i32.add
replace '1i32.add' by 'i32.add'
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add
insert '' by 'i32.add'
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add
insert '' by '0'
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add
insert '' by 'local.get'
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add
insert '' by '1i32.add'
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add
i32)
replace 'i32)' by '0'
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add
(result
replace '(result' by 'local.get'
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add
insert '' by '(result'
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add
insert '' by 'i32)'
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32.add local.get 0 i32
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add
get
If a new sentence starts here, add a space and start with an uppercase letter.
func $f (param i32) (result i32) local.get 0 local.get 0 1i32.add local.get 0 i32.add
i32.add
delete 'i32.add' by ''
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add
0
replace '0' by 'i32.'
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add
local.get
replace 'local.get' by 'i32.add'
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add
132.shl1
replace '132.shl1' by '0'
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add
1
replace '1' by 'local.get'
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add
132.const
replace '132.const' by '132.shl1'
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add
0
replace '0' by '1'
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add
local.get
replace 'local.get' by '132.const'
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add
i32)
replace 'i32)' by '0'
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add
(result
replace '(result' by 'local.get'
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add
insert '' by '(result'
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add
insert '' by 'i32)'
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add
get
If a new sentence starts here, add a space and start with an uppercase letter.
func $f (param i32) (result i32) local.get 0 132.const 1 132.shl1 local.get 0 i32.add
i32.add
delete 'i32.add' by ''
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add
0
replace '0' by 'i32.'
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add
local.get
replace 'local.get' by 'i32.add'
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add
i132.mul
replace 'i132.mul' by '0'
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add
2
replace '2' by 'local.get'
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add
1i32.const
replace '1i32.const' by 'i132.mul'
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add
0
replace '0' by '2'
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add
local.get
replace 'local.get' by '1i32.const'
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add
i32)
replace 'i32)' by '0'
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add
(result
replace '(result' by 'local.get'
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add
insert '' by '(result'
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add
insert '' by 'i32)'
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add i32.
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add
get
If a new sentence starts here, add a space and start with an uppercase letter.
func $f (param i32) (result i32) local.get 0 1i32.const 2 i132.mul local.get 0 i32.add
3:i32
replace '3:i32' by '3:i32.'
42 13 shl nsw i32 %0, 1:132 mul nsw 40, 3:i32.
42 13 shl nsw i32 %0, 1:132 mul nsw 40, 3:i32
43
replace '43' by '43.'
Add nsw i32 %0,%0 mul nsw 40, 3:i32 “2 43.
add nsw i32 %0,%0 mul nsw 40, 3:i32 “2 43
add
replace 'add' by 'Add'
Add nsw i32 %0,%0 mul nsw 40, 3:i32 “2 43.
add nsw i32 %0,%0 mul nsw 40, 3:i32 “2 43
%0,%2
replace '%0,%2' by '%'
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2
%0,2
replace '%0,2' by '%0,%2'
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2
insert '' by 'add'
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2
insert '' by '%0,%2'
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2
insert '' by 'i32'
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2
insert '' by 'nsw'
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2
insert '' by '%0,2'
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %0,%2 add nsw i32 %
h2 = 13 mul nsw i32 %0,2 add nsw i32 %0,%2
insert '' by 'nsw.'
42 13 mul nsw i32 %0,2 mul nsw 40, 3:i32 mul nsw.
42 13 mul nsw i32 %0,2 mul nsw 40, 3:i32
insert '' by 'mul'
42 13 mul nsw i32 %0,2 mul nsw 40, 3:i32 mul nsw.
42 13 mul nsw i32 %0,2 mul nsw 40, 3:i32
1:i32
delete '1:i32' by ''
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
%0,
replace '%0,' by '1:i32.'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
i32
replace 'i32' by '%0,'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
nsw
replace 'nsw' by 'i32'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
shl
replace 'shl' by 'nsw'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
=
replace '=' by 'shl'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
72
replace '72' by '='
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
%1}
replace '%1}' by '72'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
i132
replace 'i132' by '%1'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
ret
replace 'ret' by 'i132'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
10)
replace '10)' by 'ret'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
@f(i32
replace '@f(i32' by '10)'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
i32
replace 'i32' by '@f(i32'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
call
replace 'call' by 'i32'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
tail
replace 'tail' by 'call'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
=
replace '=' by 'tail'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
%1
replace '%1' by '='
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
{
replace '{' by '%1'
define i32 @main() %1 = tail call i32 @f(i32 10) ret i132 %1 72 = shl nsw i32 %0, 1:i32.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
{
Don’t put a space after the opening parenthesis.
define i32 @main() { %1 = tail call i32 @f(i32 10) ret i132 %1} 72 = shl nsw i32 %0, 1:i32
3:132
replace '3:132' by '3:132.'
72 = add nsw i32 %0,%0 %3 = mul nsw 40, 3:132.
72 = add nsw i32 %0,%0 %3 = mul nsw 40, 3:132
43
replace '43' by '43.'
Ret i32 43.
ret i32 43
ret
replace 'ret' by 'Ret'
Ret i32 43.
ret i32 43
for_code1___code_2
delete 'for_code1___code_2' by ''
define i32 @f(i32) Replacement candidates Replacement candidates for for_code1___code_2__.
define i32 @f(i32) { Replacement candidates Replacement candidates for for_code1___code_2
for
delete 'for' by ''
define i32 @f(i32) Replacement candidates Replacement candidates for for_code1___code_2__.
define i32 @f(i32) { Replacement candidates Replacement candidates for for_code1___code_2
candidates
replace 'candidates' by 'for_code1___code_2__.'
define i32 @f(i32) Replacement candidates Replacement candidates for for_code1___code_2__.
define i32 @f(i32) { Replacement candidates Replacement candidates for for_code1___code_2
Replacement
replace 'Replacement' by 'for'
define i32 @f(i32) Replacement candidates Replacement candidates for for_code1___code_2__.
define i32 @f(i32) { Replacement candidates Replacement candidates for for_code1___code_2
insert '' by 'candidates'
define i32 @f(i32) Replacement candidates Replacement candidates for for_code1___code_2__.
define i32 @f(i32) { Replacement candidates Replacement candidates for for_code1___code_2
{
replace '{' by 'Replacement'
define i32 @f(i32) Replacement candidates Replacement candidates for for_code1___code_2__.
define i32 @f(i32) { Replacement candidates Replacement candidates for for_code1___code_2
3.6:
delete '3.6:' by ''
Wasm program variants generated from program Listing 3.8.
Listing 3.6: Wasm program variants generated from program Listing 3.8.
Listing
delete 'Listing' by ''
Wasm program variants generated from program Listing 3.8.
Listing 3.6: Wasm program variants generated from program Listing 3.8.
respectively
replace 'respectively' by 'respectively,'
CROW detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 show. CROW synthesizes 2 + 1 candidate code replacements for each code, respectively, as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.
CROW, detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 shows. CROW synthesizes 2 + 1 candidate code replacements for each code respectively as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.
code
replace 'code' by 'code,'
CROW detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 show. CROW synthesizes 2 + 1 candidate code replacements for each code, respectively, as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.
CROW, detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 shows. CROW synthesizes 2 + 1 candidate code replacements for each code respectively as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.
shows.
replace 'shows.' by 'show.'
CROW detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 show. CROW synthesizes 2 + 1 candidate code replacements for each code, respectively, as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.
CROW, detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 shows. CROW synthesizes 2 + 1 candidate code replacements for each code respectively as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.
CROW,
replace 'CROW,' by 'CROW'
CROW detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 show. CROW synthesizes 2 + 1 candidate code replacements for each code, respectively, as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.
CROW, detects code_1 and code_2 as the enclosing boxes in the leftmost part of Listing 3.4 shows. CROW synthesizes 2 + 1 candidate code replacements for each code respectively as the green highlighted lines show in the rightmost parts of Listing 3.4. The baseline strategy of CROW is to generate variants out of all possible combinations of the candidate code replacements, 7.e., uses the power set of all candidate code replacements.
https://github.com/ASSERT-KTH/slumps
replace 'https://github.com/ASSERT-KTH/slumps' by 'https://github.com/ASSERT-KTH/slumps.'
Contribution paper and artifact CROW is a compiler-based approach. It leverages enumerative synthesis to generate functionally equivalent code replacements and assemble them into diverse Wasm program variants. CROW uses SMT solvers to guarantee functional equivalence. CROW is fully presented in Cabrera-Arteaga et al. “CROW: Code Randomization of WebAssembly” at proceedings of Measurements, Attacks, and Defenses for the Web (MADWeb), NDSS 2021 https://doi.org/10.14722/madweb. 2021. 23004. CROW source code is available at https://github.com/ASSERT-KTH/slumps.
Contribution paper and artifact CROW is a compiler-based approach. It leverages enumerative synthesis to generate functionally equivalent code replacements and assembles them into diverse Wasm program variants. CROW uses SMT solvers to guarantee functional equivalence. CROW is fully presented in Cabrera-Arteaga et al. “CROW: Code Randomization of WebAssembly” at proceedings of Measurements, Attacks, and Defenses for the Web (MADWeb), NDSS 2021 https://doi.org/10.14722/madweb. 2021. 23004. CROW source code is available at https://github.com/ASSERT-KTH/slumps
assembles
replace 'assembles' by 'assemble'
Contribution paper and artifact CROW is a compiler-based approach. It leverages enumerative synthesis to generate functionally equivalent code replacements and assemble them into diverse Wasm program variants. CROW uses SMT solvers to guarantee functional equivalence. CROW is fully presented in Cabrera-Arteaga et al. “CROW: Code Randomization of WebAssembly” at proceedings of Measurements, Attacks, and Defenses for the Web (MADWeb), NDSS 2021 https://doi.org/10.14722/madweb. 2021. 23004. CROW source code is available at https://github.com/ASSERT-KTH/slumps.
Contribution paper and artifact CROW is a compiler-based approach. It leverages enumerative synthesis to generate functionally equivalent code replacements and assembles them into diverse Wasm program variants. CROW uses SMT solvers to guarantee functional equivalence. CROW is fully presented in Cabrera-Arteaga et al. “CROW: Code Randomization of WebAssembly” at proceedings of Measurements, Attacks, and Defenses for the Web (MADWeb), NDSS 2021 https://doi.org/10.14722/madweb. 2021. 23004. CROW source code is available at https://github.com/ASSERT-KTH/slumps
graph
replace 'graph' by 'graphs.'
Multivariant call graphs.
Multivariant call graph
the
replace 'the' by 'The'
As illustrated in Figure 3.3, MEWE takes the LLVM IR variants generated by CROW’s diversifier. It then merges LLVM IR variants into a Wasm multivariant. In the figure, we highlight the two components of MEWE, Multivariant Generation and the Mizer. In the Multivariant Generation process, MEWE gathers the LLVM IR variants created by CROW. The Mixer component, on the other hand, links the multivariant binary and creates a new entrypoint for the binary. Creating a new entrypoint is needed in case the output of CROW are variants of the original entrypoint, e.g. The main function. Concretely, it wraps the dispatcher for the entrypoint variants as a new function for the final Wasm binary and is declared as the application entrypoint. The random generator is needed to perform the execution-path randomization. For the random generator, we rely on WASI’s specification [41] for the random behavior of the dispatchers. However, its exact implementation is dependent on the host engine on which the binary is executed. Finally, using the same custom Wasm LLVM backend as CROW, we generate a standalone multivariant Wasm binary. Once generated, the multivariant Wasm binary can be deployed to any Wasm engine.
As illustrated in Figure 3.3, MEWE takes the LLVM IR variants generated by CROW’s diversifier. It then merges LLVM IR variants into a Wasm multivariant. In the figure, we highlight the two components of MEWE, Multivariant Generation and the Mizer. In the Multivariant Generation process, MEWE gathers the LLVM IR variants created by CROW. The Mixer component, on the other hand, links the multivariant binary and creates a new entrypoint for the binary. Creating a new entrypoint is needed in case the output of CROW are variants of the original entrypoint, e.g. the main function. Concretely, it wraps the dispatcher for the entrypoint variants as a new function for the final Wasm binary and is declared as the application entrypoint. The random generator is needed to perform the execution-path randomization. For the random generator, we rely on WASI’s specification [41] for the random behavior of the dispatchers. However, its exact implementation is dependent on the host engine on which the binary is executed. Finally, using the same custom Wasm LLVM backend as CROW, we generate a standalone multivariant Wasm binary. Once generated, the multivariant Wasm binary can be deployed to any Wasm engine.
at
replace 'at' by 'At'
This section describes MEWE [37], our second technical contribution. MEWE synthesizes diversified function variants by using CROW. It then provides execution-path randomization in a Multivariant Execution (MVE) [83]. Execution path randomization is a technique that randomizes the execution path of a program at runtime, i.e. At each invocation of a function, a different variant is executed. MEWE generates application-level multivariant binaries without changing the operating system or Wasm runtime. It creates an MVE by intermixing functions for which CROW generates variants, as illustrated by the green square in Figure 3.1. MEWE inlines function variants when appropriate, resulting in call stack diversification at runtime.
This section describes MEWE [37], our second technical contribution. MEWE synthesizes diversified function variants by using CROW. It then provides execution-path randomization in a Multivariant Execution (MVE) [83]. Execution path randomization is a technique that randomizes the execution path of a program at runtime, i.e. at each invocation of a function, a different variant is executed. MEWE generates application-level multivariant binaries without changing the operating system or Wasm runtime. It creates an MVE by intermixing functions for which CROW generates variants, as illustrated by the green square in Figure 3.1. MEWE inlines function variants when appropriate, resulting in call stack diversification at runtime.
insert '' by 'the'
Figure 3.3: Overview of the MEWE workflow. It takes as input an LLVM binary. It first generates a set of functionally equivalent variants for each function in the binary using CROW. Then, MEWE generates an LLVM multivariant binary composed of all the function variants. Then, the Mixer includes the behavior in charge of selecting a variant when a function is invoked. Finally, the MEWE mixer composes the LLVM multivariant binary with a random number generation library and tampers the original application entrypoint. The final process produces a Wasm multivariant binary ready to be deployed.
Figure 3.3: Overview of MEWE workflow. It takes as input an LLVM binary. It first generates a set of functionally equivalent variants for each function in the binary using CROW. Then, MEWE generates an LLVM multivariant binary composed of all the function variants. Then, the Mixer includes the behavior in charge of selecting a variant when a function is invoked. Finally, the MEWE mixer composes the LLVM multivariant binary with a random number generation library and tampers the original application entrypoint. The final process produces a Wasm multivariant binary ready to be deployed.
Le
delete 'Le' by ''
Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I free function, functiony.
Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I freee function, functiony Le
functiony
replace 'functiony' by 'functiony.'
Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I free function, functiony.
Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I freee function, functiony Le
freee
replace 'freee' by 'free'
Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I free function, functiony.
Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I freee function, functiony Le
]
Unpaired symbol: ‘[’ seems to be missing
Multivariant Generation LVM Multivariant binary re (T | random generator entrypoint tampering Multivariant Wasm Binary LLVM Multivariant function] N J binary I freee function, functiony Le
binary
replace 'binary' by 'binary.'
Exemplifying a Multivariant binary.
Exemplifying a Multivariant binary
insert '' by 'it.'
Then there is the question of whether or not there is a need for it.
insert '' by 'for'
Then there is the question of whether or not there is a need for it.
insert '' by 'need'
Then there is the question of whether or not there is a need for it.
insert '' by 'a'
Then there is the question of whether or not there is a need for it.
insert '' by 'is'
Then there is the question of whether or not there is a need for it.
insert '' by 'there'
Then there is the question of whether or not there is a need for it.
insert '' by 'not'
Then there is the question of whether or not there is a need for it.
insert '' by 'or'
Then there is the question of whether or not there is a need for it.
insert '' by 'whether'
Then there is the question of whether or not there is a need for it.
insert '' by 'of'
Then there is the question of whether or not there is a need for it.
insert '' by 'question'
Then there is the question of whether or not there is a need for it.
insert '' by 'the'
Then there is the question of whether or not there is a need for it.
insert '' by 'Then'
Then there is the question of whether or not there is a need for it.
insert '' by 'Variant'
e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant
e Variant
insert '' by 'e'
e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant e Variant
e Variant
insert '' by 'Original'
Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original
© Original
©
replace '©' by 'Original'
Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original Original
© Original
3.7:
replace '3.7:' by '3.7.'
Listing 3.7. Dispatcher function embedded in the multivariant binary of the original function in the rightmost green node in Figure 3.4. The code is commented on for the sake of understanding.
Listing 3.7: Dispatcher function embedded in the multivariant binary of the original function in the rightmost green node in Figure 3.4. The code is commented on for the sake of understanding.
%4
delete '%4' by ''
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4i32
delete 'i32' by ''
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4ret
delete 'ret' by ''
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4(%0)
delete '(%0)' by ''
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4@foo_original
delete '@foo_original' by ''
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4call
delete 'call' by ''
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4=
delete '=' by ''
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4end:
delete 'end:' by ''
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4%3
delete '%3' by ''
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4inlined>
delete 'inlined>' by ''
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4foo_44_
delete 'foo_44_' by ''
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4delete '
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4=
replace '=' by 'body'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4%3
replace '%3' by '='
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4case_44_:
replace 'case_44_:' by '%3'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4%2
replace '%2' by 'case_44_:'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4i32
replace 'i32' by '%2'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4ret
replace 'ret' by 'i32'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4@f00_43_(%0)
replace '@f00_43_(%0)' by 'ret'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4i32
replace 'i32' by '@f00_43_(%0)'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4call
replace 'call' by 'i32'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4=
replace '=' by 'call'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %442
replace '42' by '='
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4case_43_:
replace 'case_43_:' by '42'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4%case_44_]
replace '%case_44_]' by 'case_43_:'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4label
replace 'label' by '%case_44_]'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %41,
replace '1,' by 'label'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4i32
replace 'i32' by '1,'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4%case_43_
replace '%case_43_' by 'i32'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4label
replace 'label' by '%case_43_'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %40,
replace '0,' by 'label'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4i32
replace 'i32' by '0,'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4[
replace '[' by 'i32'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4%end
replace '%end' by '['
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4label
replace 'label' by '%end'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %441,
replace '41,' by 'label'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4i32
replace 'i32' by '41,'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4switch
replace 'switch' by 'i32'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %43)
replace '3)' by 'switch'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4@discriminate(i32
replace '@discriminate(i32' by '3)'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4i32
replace 'i32' by '@discriminate(i32'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4wi
replace 'wi' by '='
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4entry:
replace 'entry:' by 'wi'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4{
replace '{' by 'entry:'
define internal i32 @foo(i32 %0) entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 = body of
define internal i32 @foo(i32 %0) { entry: wi = call i32 @discriminate(i32 3) switch i32 41, label %end [ i32 0, label %case_43_ i32 1, label %case_44_] case_43_: 42 = call i32 @f00_43_(%0) ret i32 %2 case_44_: %3 =
ret i32 %3 end: %4 = call i32 @foo_original (%0) ret i32 %4
insert '' by 'N.'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by 'Wasm'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
i.
replace 'i.' by 'Wasm'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
I
replace 'I' by 'Wasm'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
t
replace 't' by 'N.'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
'
replace ''' by 'Wasm'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
I
replace 'I' by 'N.'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
|
replace '|' by 'Wasm'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
1
replace '1' by 'N.'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
*-------traversal
replace '*-------traversal' by 'Wasm'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
generation
replace 'generation' by 'Wasm'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
(e-graph
replace '(e-graph' by 'Wasm'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
:
replace ':' by 'Wasm'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
e-graph
replace 'e-graph' by 'i.'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
,
delete ',' by ''
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
a}
delete 'a}' by ''
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
>
replace '>' by 't'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
————
replace '————' by '''
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by 'I'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by 'wasm-mutate'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by '1'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by '*-------traversal'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by 'generation'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by '(e-graph'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by ':'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by 'e-graph'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by ','
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by 'a'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by '>'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by '————'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
'
replace ''' by 'I'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
'
replace ''' by '|'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
IR
replace 'IR' by '''
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
transformed*
replace 'transformed*' by '''
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
insert '' by 'transformed*'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
binary
replace 'binary' by 'IR'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
~
replace '~' by 'binary'
binary IR transformed* IR ' ' | I ———— > a , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N. Wasm Wasm N
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
'
Don’t put a space on both sides of a quote symbol.
~ binary IR transformed* IR ' ' | I ———— > a} , I e-graph : (e-graph 1 generation *-------traversal 1 wasm-mutate | I I ' t I i. Wasm Wasm N
https://github.com/ASSERT-KTH/MEWE
replace 'https://github.com/ASSERT-KTH/MEWE' by 'https://github.com/ASSERT-KTH/MEWE.'
Contribution paper and artifact MEWE provides dynamic execution path randomization by packaging variants generated out of CROW. MEWE is fully presented in Cabrera-Arteaga et al. “Multi-Variant Execution at the Edge” Proceedings of Moving Target Defense, 2022, ACM https://dl.acm.o rg/doi/abs/10.1145/3560828. 3564007 MEWE is also available as an open-source tool at https://github.com/ASSERT-KTH/MEWE.
Contribution paper and artifact MEWE provides dynamic execution path randomization by packaging variants generated out of CROW. MEWE is fully presented in Cabrera-Arteaga et al. “Multi-Variant Execution at the Edge” Proceedings of Moving Target Defense, 2022, ACM https://dl.acm.o rg/doi/abs/10.1145/3560828. 3564007 MEWE is also available as an open-source tool at https://github.com/ASSERT-KTH/MEWE
meta-rule,
replace 'meta-rule,' by 'meta-rule'
Rewriting rules inside the Peephole meta-rule operate over the data flow graph of instructions within a function body, representing the lowest level of rewriting. In WASM-MUTATE, we have implemented 125 rewriting rules specifically for this category, each one avoiding targeting instructions that might induce undefined behavior, e.g., function calls.
Rewriting rules inside the Peephole meta-rule, operate over the data flow graph of instructions within a function body, representing the lowest level of rewriting. In WASM-MUTATE, we have implemented 125 rewriting rules specifically for this category, each one avoiding targeting instructions that might induce undefined behavior, e.g., function calls.
enhancing
replace 'enhancing' by 'Enhancing'
WASM-MUTATE contains a comprehensive set of 135 rewriting rules. In this context, a rewriting rule is a triple (LHS, RHS, Cond) where LHS specifies the segment of binary targeted for replacement, RHS describes its functionally equivalent substitute, and Cond outlines the conditions that must be met for the replacement to take place, e.g. Enhancing type constraints. WASM-MUTATE groups these rewriting rules into meta-rules depending on their target inside a Wasm binary, ranging from high-level changes affecting binary section structure to low-level modifications within the code section. This section focuses on the biggest meta-rule implemented in WASM-MUTATE, the Peephole meta-rule?.
WASM-MUTATE contains a comprehensive set of 135 rewriting rules. In this context, a rewriting rule is a tuple (LHS, RHS, Cond) where LHS specifies the segment of binary targeted for replacement, RHS describes its functionally equivalent substitute, and Cond outlines the conditions that must be met for the replacement to take place, e.g. enhancing type constraints. WASM-MUTATE groups these rewriting rules into meta-rules depending on their target inside a Wasm binary, ranging from high-level changes affecting binary section structure to low-level modifications within the code section. This section focuses on the biggest meta-rule implemented in WASM-MUTATE, the Peephole meta-rule?.
tuple
replace 'tuple' by 'triple'
WASM-MUTATE contains a comprehensive set of 135 rewriting rules. In this context, a rewriting rule is a triple (LHS, RHS, Cond) where LHS specifies the segment of binary targeted for replacement, RHS describes its functionally equivalent substitute, and Cond outlines the conditions that must be met for the replacement to take place, e.g. Enhancing type constraints. WASM-MUTATE groups these rewriting rules into meta-rules depending on their target inside a Wasm binary, ranging from high-level changes affecting binary section structure to low-level modifications within the code section. This section focuses on the biggest meta-rule implemented in WASM-MUTATE, the Peephole meta-rule?.
WASM-MUTATE contains a comprehensive set of 135 rewriting rules. In this context, a rewriting rule is a tuple (LHS, RHS, Cond) where LHS specifies the segment of binary targeted for replacement, RHS describes its functionally equivalent substitute, and Cond outlines the conditions that must be met for the replacement to take place, e.g. enhancing type constraints. WASM-MUTATE groups these rewriting rules into meta-rules depending on their target inside a Wasm binary, ranging from high-level changes affecting binary section structure to low-level modifications within the code section. This section focuses on the biggest meta-rule implemented in WASM-MUTATE, the Peephole meta-rule?.
Rules
replace 'Rules' by 'Rules.'
WebAssembly Rewriting Rules.
WebAssembly Rewriting Rules
I multiple
A verb or adverb may be missing or misspelled.
Definition 2 (Stacked transformation) Given an original input WebAssembly binary I and a diversifier D, stacked transformations are defined as the application of D over the binary I multiple times, t.e., D(D(D(...(1)))). Notice that, the number of stacked transformations is the number of times the diversifier D is applied.
transformation
replace 'transformation' by 'transformation.'
WASM-MUTATE applies one transformation at a time. Notice that the output of one applied transformation can be chained again as an input WebAssembly binary, enabling the generation of many variants, leading us to enunciate the notion of Stacked transformation.
WASM-MUTATE applies one transformation at a time. Notice that, the output of one applied transformation can be chained again as an input WebAssembly binary, enabling the generation of many variants, leading us to enunciate the notion of Stacked transformation
that,
replace 'that,' by 'that'
WASM-MUTATE applies one transformation at a time. Notice that the output of one applied transformation can be chained again as an input WebAssembly binary, enabling the generation of many variants, leading us to enunciate the notion of Stacked transformation.
WASM-MUTATE applies one transformation at a time. Notice that, the output of one applied transformation can be chained again as an input WebAssembly binary, enabling the generation of many variants, leading us to enunciate the notion of Stacked transformation
an
replace 'an' by 'An'
Figure 3.5 illustrates the workflow of WASM-MUTATE, which initiates with a WebAssembly binary as its input. The first step involves parsing this binary to create suitable abstractions, e.g. An intermediate representation. Subsequently, WASM-MUTATE uses predefined rewriting rules to construct an e-graph for the initial program, encapsulating all potential equivalent codes derived from the rewriting rules. The assurance of functional equivalence is rooted in the inherent properties of the individual rewrite rules employed. Then, pieces of the original program are randomly substituted by the result of random e-graph traversals, resulting in a variant that maintains functional equivalence to the original binary.
Figure 3.5 illustrates the workflow of WASM-MUTATE, which initiates with a WebAssembly binary as its input. The first step involves parsing this binary to create suitable abstractions, e.g. an intermediate representation. Subsequently, WASM-MUTATE uses predefined rewriting rules to construct an e-graph for the initial program, encapsulating all potential equivalent codes derived from the rewriting rules. The assurance of functional equivalence is rooted in the inherent properties of the individual rewrite rules employed. Then, pieces of the original program are randomly substituted by the result of random e-graph traversals, resulting in a variant that maintains functional equivalence to the original binary.
-i32.rand
replace '-i32.rand' by '-i32.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand'
Cond y = x -i32.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand.rand
Cond y = x -i32.rand
x
replace 'x' by 'x.'
LHS i32.const x.
LHS i32.const x
Concretely,
replace 'Concretely,' by 'Concretly,'
Moreover, we augment the internal representation of a Wasm program to bolster WASM-MUTATE’s transformation capabilities through the Peephole meta-rule. Concretly, we augment the parsing stage in WASM-MUTATE by including custom operator instructions. These custom operator instructions are designed to use well-established code diversification techniques through rewriting rules. When converting back to the WebAssembly binary format from the intermediate representation, custom instructions are meticulously handled to retain the original functionality of the WebAssembly program.
Moreover, we augment the internal representation of a Wasm program to bolster WASM-MUTATE’s transformation capabilities through the Peephole meta-rule. Concretely, we augment the parsing stage in WASM-MUTATE by including custom operator instructions. These custom operator instructions are designed to use well-established code diversification techniques through rewriting rules. When converting back to the WebAssembly binary format from the intermediate representation, custom instructions are meticulously handled to retain the original functionality of the WebAssembly program.
omitting
delete 'omitting' by ''
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
In
delete 'In' by ''
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
operands.
replace 'operands.' by 'Omitting'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
e-class’s
replace 'e-class’s' by 'operands.'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
corresponding
replace 'corresponding' by 'e-class’s'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
the
replace 'the' by 'corresponding'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
within
replace 'within' by 'the'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
options
replace 'options' by 'within'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
available
replace 'available' by 'options'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
the
replace 'the' by 'available'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
from
replace 'from' by 'the'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
size
replace 'size' by 'from'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
minimal
replace 'minimal' by 'size'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
the
replace 'the' by 'minimal'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
with
replace 'with' by 'the'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
AST
replace 'AST' by 'with'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
the
replace 'the' by 'AST'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
selecting
replace 'selecting' by 'the'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
subsequently
replace 'subsequently' by 'selecting'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
e-node,
replace 'e-node,' by 'subsequently'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
specific
replace 'specific' by 'e-node,'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
a
replace 'a' by 'specific'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
at
replace 'at' by 'a'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
extraction
replace 'extraction' by 'at'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
the
replace 'the' by 'extraction'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
commence
replace 'commence' by 'the'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
might
replace 'might' by 'commence'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
one
replace 'one' by 'might'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
e-graph,
replace 'e-graph,' by 'one'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
an
replace 'an' by 'e-graph,'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
from
replace 'from' by 'an'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
code
replace 'code' by 'from'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
“optimal”
replace '“optimal”' by 'code'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
the
replace 'the' by '“optimal”'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
extract
replace 'extract' by 'the'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
To
replace 'To' by 'extract'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
[146].
replace '[146].' by 'To'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
sequence
replace 'sequence' by '[146].'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
application
replace 'application' by 'sequence'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
their
replace 'their' by 'application'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
of
replace 'of' by 'their'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
regardless
replace 'regardless' by 'of'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
rules,
replace 'rules,' by 'regardless'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
optimization
replace 'optimization' by 'rules,'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
multiple
replace 'multiple' by 'optimization'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
from
replace 'from' by 'multiple'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
optimal
replace 'optimal' by 'code'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
the
replace 'the' by 'optimal'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
highlight
replace 'highlight' by 'Highlight'
Willsey et al. Highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. Omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
Willsey et al. highlight the potential for high flexibility in extracting code fragments from e-graphs, a process that can be recursively orchestrated through a cost function applied to e-nodes and their respective operands. This methodology ensures the functional equivalence of the derived code [143]. For instance, e-graphs address the challenge of generating the optimal code from multiple optimization rules, regardless of their application sequence [146]. To extract the “optimal” code from an e-graph, one might commence the extraction at a specific e-node, subsequently selecting the AST with the minimal size from the available options within the corresponding e-class’s operands. In omitting the cost function from the extraction strategy leads us to a significant property: any path navigated through the e-graph yields a functionally equivalent code variant.
traversal
replace 'traversal' by 'traversal.'
E-Graph traversal.
E-Graph traversal
insert '' by 'of'
Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8, a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instance of i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.
Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8 a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instanceof i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.
instanceof
replace 'instanceof' by 'instance'
Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8, a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instance of i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.
Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8 a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instanceof i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.
3.8
replace '3.8' by '3.8,'
Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8, a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instance of i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.
Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8 a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instanceof i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.
,)
Put a space after the comma.
Let us illustrate how WASM-MUTATE generates variant programs by using the algorithm enunciated previously. Here, we use Algorithm 1 with a maximum depth of 1. In Listing 3.8 a hypothetical original Wasm binary is illustrated. In this context, a potential user has set two pivotal rewriting rules: (x, container (x nop),) and (x, x i32.add 0, x instanceof i32). The former rule grants the ability to append a nop instruction to any subexpression, a well-known low-level diversification strategy [82]. The latter rule adds zero to any numeric value.
expr
delete 'expr' by ''
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
11:
delete '11:' by ''
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
{subexpr}
delete '{subexpr}' by ''
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
U
delete 'U' by ''
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
expr.operands
delete 'expr.operands' by ''
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
<
delete '<' by ''
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
10:
delete '10:' by ''
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
1)
delete '1)' by ''
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
—
delete '—' by ''
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
depth
delete 'depth' by ''
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
child,
delete 'child,' by ''
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
TRAVERSE(egraph,
delete 'TRAVERSE(egraph,' by ''
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
~
delete '~' by ''
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
subexpr
replace 'subexpr' by 'TRAVERSE(e'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
9:
replace '9:' by 'subexpr'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
do
replace 'do' by '9:'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
node.children
replace 'node.children' by 'do'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
€
replace '€' by 'node.children'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
child
replace 'child' by '€'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
each
replace 'each' by 'child'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
for
replace 'for' by 'each'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
8
replace '8' by 'for'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
|])
replace '|])' by '8'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
=
replace '=' by '|]'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
operands
replace 'operands' by '='
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
(node,
replace '(node,' by 'operands'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
<
replace '<' by '(node,'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
<—
replace '<—' by '—'
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node — random_choice(nodes) expr (node, operands = |] 8 for each child € node.children do 9: subexpr TRAVERSE(e
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
]
Unpaired symbol: ‘[’ seems to be missing
Algorithm 1 e-graph traversal algorithm. 1: procedure TRAVERSE(egraph, eclass, depth) 2: if depth = 0 then 3: return smallest_tree_from(egraph, eclass) 4: else 5: nodes + egraphleclass] 6: node <— random_choice(nodes) expr < (node, operands = |]) 8 for each child € node.children do 9: subexpr ~ TRAVERSE(egraph, child, depth — 1) 10: expr.operands < expr.operands U {subexpr} 11: return expr
node
Possible agreement error. The noun ‘node’ seems to be countable.
The algorithm initiates at the e-class with the instruction i64.const 1, as seen in Listing 3.8. At (2), it randomly selects an equivalent node within the e-class, in this instance taking the i64.add node, resulting: expr = i64.add 1 r. As the traversal advances, it follows on the left operand of the previously chosen node, settling on the i64.const 0 node within the same e-class (3). Then, the right operand of the i64.add node is selected, selecting the container(4) operator yielding: expr = i64.or (i64.const 0 container (r nop)). The algorithm chooses the right operand of the container (5), which correlates to the initial instruction e-node highlighted in (6), culminating in the final expression: expr = i64.or (i64.const 0 container(i64.const 1 nop)) i64.const 1. As we proceed to the encoding phases, the container operator is ignored as a real Wasm instruction, finally resulting in the program in Listing 3.9.
i64)))
replace 'i64)))' by 'i64))'
(module (type (;0;) (func (param i32 £32) (result i64)) (func (;0;) (type 0) (param i32 £32) (result i64) i64.const 1)) Listing 3.8: Wasm function.
(module (type (;0;) (func (param i32 £32) (result i64))) (func (;0;) (type 0) (param i32 £32) (result i64) i64.const 1)) Listing 3.8: Wasm function.
(
Unpaired symbol: ‘)’ seems to be missing
(module (type (;0;) (func (param i32 £32) (result i64))) (func (;0;) (type 0) (param i32 £32) (result i64) i64.const 1)) Listing 3.8: Wasm function.
3.6.
replace '3.6.' by '3.6'
(module (type (;0;) (func (param i32 £32) (result i64)) (func (;0;) (type 0) (param i32 £32) (result i64) (i64.add (i64.const 0 1i64.const 1 nop))) Listing 3.9: Random peephole mutation using e-graph traversal for Listing 3.8 over e-graph Figure 3.6 The textual format is folded for better understanding.
(module (type (;0;) (func (param i32 £32) (result i64))) (func (;0;) (type 0) (param i32 £32) (result i64) (i64.add (i64.const 0 1i64.const 1 nop))) Listing 3.9: Random peephole mutation using e-graph traversal for Listing 3.8 over e-graph Figure 3.6. The textual format is folded for better understanding.
i64)))
replace 'i64)))' by 'i64))'
(module (type (;0;) (func (param i32 £32) (result i64)) (func (;0;) (type 0) (param i32 £32) (result i64) (i64.add (i64.const 0 1i64.const 1 nop))) Listing 3.9: Random peephole mutation using e-graph traversal for Listing 3.8 over e-graph Figure 3.6 The textual format is folded for better understanding.
(module (type (;0;) (func (param i32 £32) (result i64))) (func (;0;) (type 0) (param i32 £32) (result i64) (i64.add (i64.const 0 1i64.const 1 nop))) Listing 3.9: Random peephole mutation using e-graph traversal for Listing 3.8 over e-graph Figure 3.6. The textual format is folded for better understanding.
Algorithm
replace 'Algorithm' by 'algorithm'
Notice that, within the e-graph showcased in Figure 3.6, the container node maintains equivalence across all e-classes. Consequently, increasing the depth parameter in algorithm 1 would potentially escalate the number of viable variants infinitely.
Notice that, within the e-graph showcased in Figure 3.6, the container node maintains equivalence across all e-classes. Consequently, increasing the depth parameter in Algorithm 1 would potentially escalate the number of viable variants infinitely.
insert '' by 'it.'
Then there is the question of whether or not there is a need for it.
insert '' by 'for'
Then there is the question of whether or not there is a need for it.
insert '' by 'need'
Then there is the question of whether or not there is a need for it.
insert '' by 'a'
Then there is the question of whether or not there is a need for it.
insert '' by 'is'
Then there is the question of whether or not there is a need for it.
insert '' by 'there'
Then there is the question of whether or not there is a need for it.
insert '' by 'not'
Then there is the question of whether or not there is a need for it.
insert '' by 'or'
Then there is the question of whether or not there is a need for it.
insert '' by 'whether'
Then there is the question of whether or not there is a need for it.
insert '' by 'of'
Then there is the question of whether or not there is a need for it.
insert '' by 'question'
Then there is the question of whether or not there is a need for it.
insert '' by 'the'
Then there is the question of whether or not there is a need for it.
insert '' by 'Then'
Then there is the question of whether or not there is a need for it.
selection
replace 'selection' by 'selection.'
Random e-node selection.
Random e-node selection
selection
delete 'selection' by ''
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
e-node
delete 'e-node' by ''
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
Random
replace 'Random' by 'selection.'
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
relation
replace 'relation' by 'e-node'
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
Operator-Operand
replace 'Operator-Operand' by 'Random'
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
Operand
replace 'Operand' by 'relation'
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
Left
replace 'Left' by 'Operator-Operand'
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
Right
replace 'Right' by 'Left'
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
Left
replace 'Left' by 'Right'
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
container
replace 'container' by 'Operand'
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
Operand
replace 'Operand' by 'container'
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
i
replace 'i' by 'nop'
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
insert '' by 'i'
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
insert '' by 'Operand'
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection.
oe XS Left Operand eyclass operand S. fay a i64.const 0 | container e-class) ()) container i64.add " Right Left Operand Operand i Operand nop container Left Operand Right Operand Left Operand Operator-Operand relation Random e-node selection
“https://bytecodealliance.org/
replace '“https://bytecodealliance.org/' by '“https://bytecodealliance.org/”'
Contribution paper and artifact WASM-MUTATE uses rewriting rules and random traversals over e-graphs to provide a binary-based solution for WebAssembly diversification. WASM-MUTATE is fully presented in Cabrera-Arteaga et al. “WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly” Computers & Security, 2024. https://www.sciencedirect.com/science/article/pii/S0167404824000 324. WASM-MUTATE is available at https://github.com/bytecodealliance/wasm-t ools/tree/main/crates/wasm-mutate as a contribution to the Bytecode Alliance organization “. The Bytecode Alliance is dedicated to creating secure new software foundations, building on standards such as WebAssembly and WASI. “https://bytecodealliance.org/”
Contribution paper and artifact WASM-MUTATE uses rewriting rules and random traversals over e-graphs to provide a binary-based solution for WebAssembly diversification. WASM-MUTATE is fully presented in Cabrera-Arteaga et al. “WASM-MUTATE: Fast and Effective Binary Diversification for WebAssembly” Computers & Security, 2024. https://www.sciencedirect.com/science/article/pii/S0167404824000 324. WASM-MUTATE is available at https://github.com/bytecodealliance/wasm-t ools/tree/main/crates/wasm-mutate as a contribution to the Bytecode Alliance organization “. The Bytecode Alliance is dedicated to creating secure new software foundations, building on standards such as WebAssembly and WASI. “https://bytecodealliance.org/
Variants
An apostrophe may be missing.
On the other hand, CROW and MEWE, by using enumerative synthesis, ensure that the generated variants are more preserved than the variants created by WASM-MUTATE. In other words, the transformations generated out of CROW and MEWE are virtually irreversible by JIT compilers, such as V8 and wasmtime. This phenomenon is highlighted in the Variants strength column of Table 3.1, where we show that CROW and MEWE generate variants with 96% of preservation against 75% of WASM-MUTATE. High preservation is especially important where the preservation of the diversification is crucial, e.g., to hinder reverse engineering.
with
replace 'with' by 'With'
Tool Input Core Variants | Target Variants | Security applications in lh Strength CROW Source code or | Enumerative > 1k Code 96% Hinders static analysis and LLVM Ir synthesis section reverse engineering. With functional equivalence proved through SMT solvers MEWE Source code or | CROW, > 1k Code and | 96% Hinders static and LL VM Ir Multivariant Function dynamic analysis, reverse execution sections engineering and web timing-based attacks. WASM-Wasm binary | hand-made > 10k All Web-| 76% Hinders signature-MUTATE rewriting rules, Assembly based identification, and e-sections cache timing side-channel graph random traversals attacks.
Tool Input Core Variants | Target Variants | Security applications in lh Strength CROW Source code or | Enumerative > 1k Code 96% Hinders static analysis and LLVM Ir synthesis section reverse engineering. with functional equivalence proved through SMT solvers MEWE Source code or | CROW, > 1k Code and | 96% Hinders static and LL VM Ir Multivariant Function dynamic analysis, reverse execution sections engineering and web timing-based attacks. WASM-Wasm binary | hand-made > 10k All Web-| 76% Hinders signature-MUTATE rewriting rules, Assembly based identification, and e-sections cache timing side-channel graph random traversals attacks.
applications
replace 'applications' by 'applications.'
Security applications.
Security applications
and,
replace 'and,' by 'and'
WRN cr neha CROW, MEWE and WASM-MUTATE generate WebAssembly variants that can be used to enhance security. Overall, they generate variants that are suitable for hardening static and dynamic analysis, side-channel attacks, and thwarting signature-based identification.
WRN cr neha CROW, MEWE and WASM-MUTATE generate WebAssembly variants that can be used to enhance security. Overall, they generate variants that are suitable for hardening static and dynamic analysis, side-channel attacks, and, thwarting signature-based identification.
increase
replace 'increase' by 'increasing'
Last but not least, our technical contributions enhance security against web timing-based attacks [147, 148] by creating variants that exhibit a wide range of execution times, including faster variants compared to the original program. This strategy is especially prominent in MEWE’s approach, which develops multivariants functioning on randomizing execution paths, thereby thwarting attempts at timing-based inference attacks [148]. Adding another layer of benefit from MEWE, the integration of diverse variants into multivariants can potentially disrupt dynamic reverse engineering tools such as symbolic executors [96]. Concretely, different control flows through a random discriminator, exponentially increasing the number of possible execution paths, making multivariant binaries virtually unexplorable.
Last but not least, our technical contributions enhance security against web timing-based attacks [147, 148] by creating variants that exhibit a wide range of execution times, including faster variants compared to the original program. This strategy is especially prominent in MEWE’s approach, which develops multivariants functioning on randomizing execution paths, thereby thwarting attempts at timing-based inference attacks [148]. Adding another layer of benefit from MEWE, the integration of diverse variants into multivariants can potentially disrupt dynamic reverse engineering tools such as symbolic executors [96]. Concretely, different control flows through a random discriminator, exponentially increase the number of possible execution paths, making multivariant binaries virtually unexplorable.
Knuth
replace 'Knuth' by 'Knuth.'
— Donald Knuth.
— Donald Knuth
FOR
delete 'FOR' by ''
ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI
ASSESSING SOFTWARE DIVERSIFICATION FOR
DIVERSIFICATION
replace 'DIVERSIFICATION' by 'DI'
ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI
ASSESSING SOFTWARE DIVERSIFICATION FOR
insert '' by 'OF'
ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI
ASSESSING SOFTWARE DIVERSIFICATION FOR
insert '' by 'ASSESSMENT'
ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI
ASSESSING SOFTWARE DIVERSIFICATION FOR
insert '' by 'FOR'
ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI
ASSESSING SOFTWARE DIVERSIFICATION FOR
insert '' by 'DIVERSIFICATION'
ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI
ASSESSING SOFTWARE DIVERSIFICATION FOR
insert '' by 'SOFTWARE'
ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI
ASSESSING SOFTWARE DIVERSIFICATION FOR
insert '' by 'ASSESSING'
ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSING SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DIVERSIFICATION FOR ASSESSMENT OF SOFTWARE DI
ASSESSING SOFTWARE DIVERSIFICATION FOR
insert '' by 'hardware.'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
insert '' by 'and'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
both
replace 'both' by 'software'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
for
replace 'for' by 'both'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
Diversification
replace 'Diversification' by 'for'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
Software
replace 'Software' by 'Diversification'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
of
replace 'of' by 'Software'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
application
replace 'application' by 'of'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
the
replace 'the' by 'application'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
illustrate
replace 'illustrate' by 'the'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
we
replace 'we' by 'illustrate'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
chapter,
replace 'chapter,' by 'we'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
this
replace 'this' by 'chapter,'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
N
replace 'N' by 'this'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
I
replace 'I' by 'In'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
offensive
replace 'offensive' by 'Offensive'
Offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. In this chapter, we illustrate the application of Software Diversification for both software and hardware.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
offensive
This sentence does not start with an uppercase letter.
offensive and defensive purposes. We discuss two selected use cases that demonstrate the practical applications of our contributions. Additionally, we discuss the challenges and benefits arising from the application of Software Diversification to WebAssembly. I N this chapter, we illustrate the application of Software Diversification for both
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server
delete 'server' by ''
User browser Remote mining pool or server.
User browser © Remote mining pool or server
or
replace 'or' by 'server.'
User browser Remote mining pool or server.
User browser © Remote mining pool or server
pool
replace 'pool' by 'or'
User browser Remote mining pool or server.
User browser © Remote mining pool or server
mining
replace 'mining' by 'pool'
User browser Remote mining pool or server.
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Remote
replace 'Remote' by 'mining'
User browser Remote mining pool or server.
User browser © Remote mining pool or server
©
replace '©' by 'Remote'
User browser Remote mining pool or server.
User browser © Remote mining pool or server
into
replace 'into' by 'in'
The primary malicious use of WebAssembly in browsers is cryptojacking [66]. This is due to the essence of cryptojacking, the faster the mining, the better. Let us illustrate how a malicious WebAssembly binary is involved in browser cryptojacking. Figure 4.1 illustrates a browser attack scenario: a practical WebAssembly cryptojacking attack consists of three components: a WebAssembly binary, a JavaScript wrapper, and a backend cryptominer pool. The WebAssembly binary is responsible for executing the hash calculations, which consume significant computational resources. The JavaScript wrapper facilitates the communication between the WebAssembly binary and the cryptominer pool.
The primary malicious use of WebAssembly in browsers is cryptojacking [66]. This is due to the essence of cryptojacking, the faster the mining, the better. Let us illustrate how a malicious WebAssembly binary is involved into browser cryptojacking. Figure 4.1 illustrates a browser attack scenario: a practical WebAssembly cryptojacking attack consists of three components: a WebAssembly binary, a JavaScript wrapper, and a backend cryptominer pool. The WebAssembly binary is responsible for executing the hash calculations, which consume significant computational resources. The JavaScript wrapper facilitates the communication between the WebAssembly binary and the cryptominer pool.
evasion
replace 'evasion' by 'evasion.'
Offensive Diversification: Malware evasion.
Offensive Diversification: Malware evasion
evasion
replace 'evasion' by 'evasion.'
Cryptojacking defense evasion.
Cryptojacking defense evasion
insert '' by 'They'
Both antivirus software and browsers have implemented measures to detect cryptojacking. For instance, Firefox employs deny lists to detect cryptomining activities [149]. The academic community has also contributed to the body of work on detecting or preventing WebAssembly-based cryptojacking, as outlined in Section 2.1.5. However, malicious actors can employ evasion techniques to circumvent these detection mechanisms. Bhansali et al. They are among the first who have investigated how WebAssembly cryptojacking could potentially evade detection [67], highlighting the critical importance of this use case. The case illustrated in the subsequent sections uses Offensive Software Diversification for evading malware detection in WebAssembly.
Both antivirus software and browsers have implemented measures to detect cryptojacking. For instance, Firefox employs deny lists to detect cryptomining activities [149]. The academic community has also contributed to the body of work on detecting or preventing WebAssembly-based cryptojacking, as outlined in Section 2.1.5. However, malicious actors can employ evasion techniques to circumvent these detection mechanisms. Bhansali et al. are among the first who have investigated how WebAssembly cryptojacking could potentially evade detection [67], highlighting the critical importance of this use case. The case illustrated in the subsequent sections uses Offensive Software Diversification for evading malware detection in WebAssembly.
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@ User browser ‘QO Malware @ Remote detector QY) mining pool or server Malware) evasion technique BI)
insert '' by 'Method'
Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Method
Methodology
insert '' by 'Methodology'
Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Method
Methodology
)
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1. The user loads a webpage infected with cryptojacking malware, which leverages network resources for execution—corresponding to qa) and 2) in Figure 4.2.
that,
replace 'that,' by 'that'
Based on the two types of malware oracles and diversification configurations, we examine three scenarios: 1) VirusTotal with a feedback-guided diversification, 2) VirusTotal with a stochastic diversification, and 3) MINOS with a stochastic diversification. Notice that the fourth scenario with MINOS and feedback-guided diversification is not feasible, as MINOS is a binary oracle and cannot provide the numerical values required for feedback-guided diversification.
Based on the two types of malware oracles and diversification configurations, we examine three scenarios: 1) VirusTotal with a feedback-guided diversification, 2) VirusTotal with a stochastic diversification, and 3) MINOS with a stochastic diversification. Notice that, the fourth scenario with MINOS and feedback-guided diversification is not feasible, as MINOS is a binary oracle and cannot provide the numerical values required for feedback-guided diversification.
Diversification)
replace 'Diversification)' by 'diversification)'
Definition 4 (Feedback-guided diversification) In feedback-guided diversification, the transformation process of a WebAssembly program is guided by a numeric oracle, which influences the probability of each transformation. For instance, WASM-MUTATE can be configured to apply transformations that minimize the oracle’s confidence score. Note that feedback-guided diversification needs a numeric oracle.
Definition 4 (Feedback-guided Diversification) In feedback-guided diversification, the transformation process of a WebAssembly program is guided by a numeric oracle, which influences the probability of each transformation. For instance, WASM-MUTATE can be configured to apply transformations that minimize the oracle’s confidence score. Note that feedback-guided diversification needs a numeric oracle.
identify
replace 'identify' by 'identified'
We use the wasmbench dataset [45] to establish a ground truth. After running the wasmbench dataset through VirusTotal and MINOS, we identified 33 binaries that are: 1) flagged as malicious by at least one VirusTotal vendor and, 2) are also detected by MINOS. Then, to simulate the evasion scenario in Figure 4.2, we use WASM-MUTATE to generate WebAssembly binary variants to evade malware detection ((4) in Figure 4.2). We use WASM-MUTATE in two configurations: feedback-guided and stochastic diversification.
We use the wasmbench dataset [45] to establish a ground truth. After running the wasmbench dataset through VirusTotal and MINOS, we identify 33 binaries that are: 1) flagged as malicious by at least one VirusTotal vendor and, 2) are also detected by MINOS. Then, to simulate the evasion scenario in Figure 4.2, we use WASM-MUTATE to generate WebAssembly binary variants to evade malware detection ((4) in Figure 4.2). We use WASM-MUTATE in two configurations: feedback-guided and stochastic diversification.
Results
replace 'Results' by 'Results:'
Results:
Results
solely
delete 'solely' by ''
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
section
delete 'section' by ''
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
MINOS
replace 'MINOS' by 'only'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
The
replace 'The' by 'section'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
better.
replace 'better.' by 'MINOS'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
the
replace 'the' by 'The'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
lower,
replace 'lower,' by 'better.'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
best,
delete 'best,' by ''
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
setups
replace 'setups' by 'lower,'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
diversification
replace 'diversification' by 'the'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
feedback-guided
replace 'feedback-guided' by 'best,'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
or
replace 'or' by 'setups'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
stochastic
delete 'stochastic' by ''
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
the
replace 'the' by 'feedback-guided'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
which
replace 'which' by 'or'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
for
replace 'for' by 'diversification'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
values
replace 'values' by 'stochastic'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
the
replace 'the' by 'which'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
text
replace 'text' by 'for'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
bold
replace 'bold' by 'values'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
in
replace 'in' by 'the'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
highlight
replace 'highlight' by 'text'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
We
replace 'We' by 'bold'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
sample.
replace 'sample.' by 'in'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
each
replace 'each' by 'highlight'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
for
replace 'for' by 'We'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
#trans.)
replace '#trans.)' by 'sample.'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
(Mean
replace '(Mean' by 'each'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
required
replace 'required' by 'for'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
transformations
replace 'transformations' by '#trans.)'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
of
replace 'of' by '(Mean'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
number
replace 'number' by 'required'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
average
replace 'average' by 'transformations'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
the
replace 'the' by 'of'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
and
replace 'and' by 'number'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
#evaded),
replace '#evaded),' by 'average'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
(Max.
replace '(Max.' by 'the'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
detectors
replace 'detectors' by 'and'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
evaded
replace 'evaded' by '#evaded),'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
successfully
replace 'successfully' by '(Max.'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
of
replace 'of' by 'detectors'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
number
replace 'number' by 'evaded'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
maximum
replace 'maximum' by 'successfully'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
(#D),
replace '(#D),' by 'number'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
malware
replace 'malware' by 'maximum'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
as
replace 'as' by 'the'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
binary
replace 'binary' by '(#D),'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
original
replace 'original' by 'malware'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
the
replace 'the' by 'as'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
flag
replace 'flag' by 'binary'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
that
replace 'that' by 'original'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
vendors
replace 'vendors' by 'the'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
of
replace 'of' by 'that'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
number
replace 'number' by 'vendors'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
the
replace 'the' by 'VirusTotal'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
indicate
replace 'indicate' by 'of'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
columns
replace 'columns' by 'number'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
subsections,
delete 'subsections,' by ''
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
these
replace 'these' by 'indicate'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In
replace 'In' by 'columns'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
diversification.
replace 'diversification.' by 'the'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
feedback-guided
replace 'feedback-guided' by 'subsections,'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
and
replace 'and' by 'these'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
stochastic
replace 'stochastic' by 'In'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
employ:
replace 'employ:' by 'diversification.'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
we
replace 'we' by 'feedback-guided'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
configurations
replace 'configurations' by 'and'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
diversification
replace 'diversification' by 'stochastic'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
two
replace 'two' by 'employ:'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
the
replace 'the' by 'we'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
on
replace 'on' by 'configurations'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
based
replace 'based' by 'diversification'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
results
replace 'results' by 'two'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
insert '' by 'on'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
insert '' by 'based'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
insert '' by 'results'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
insert '' by 'the'
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section only includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
In Table 4.1, we present a comprehensive summary of the evasion experiments presented in [39], focusing on two oracles: VirusTotal and MINOS [28]. The table is organized into two main categories to separate the results for each malware oracle. For VirusTotal, we further subdivide the results based on the two diversification configurations we employ: stochastic and feedback-guided diversification. In these subsections, the columns indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which the stochastic diversification or feedback-guided diversification setups best, the lower, the better. The MINOS section solely includes a column that specifies the number of transformations needed for complete evasion. The table has 33 + 1 rows, each representing a unique WebAssembly malware study subject. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
38
delete '38' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
131
delete '131' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
218
delete '218' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
Median
delete 'Median' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
15
delete '15' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
488
delete '488' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
1
delete '1' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
120
delete '120' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
e09c32c5
delete 'e09c32c5' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
33
delete '33' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
2
delete '2' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
118
delete '118' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
5bc53343
delete '5bc53343' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
11
delete '11' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
127
delete '127' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
f0b24409
delete 'f0b24409' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
137
delete '137' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
68ca7c0e
delete '68ca7c0e' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
145
delete '145' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
a27b45ef
delete 'a27b45ef' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
143
delete '143' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
6b8c7899
delete '6b8c7899' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
66
delete '66' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
132
delete '132' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
140
delete '140' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
dceaf65b
delete 'dceaf65b' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
130
delete '130' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
clbe4071
delete 'clbe4071' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
68
delete '68' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
123
delete '123' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
153
delete '153' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
089dd312
delete '089dd312' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
it
delete 'it' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
18
delete '18' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
104
delete '104' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
119c53eb
delete '119c53eb' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
124
delete '124' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
a74a7cb8
delete 'a74a7cb8' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
108
delete '108' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
107
delete '107' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
431
delete '431' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
89a3645c
delete '89a3645c' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
59955b4c
delete '59955b4c' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
65debcbe
delete '65debcbe' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
il
delete 'il' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
72
delete '72' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
3.
delete '3.' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
295
delete '295' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
3
delete '3' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
4cbdbbb1
delete '4cbdbbb1' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
960
delete '960' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
34
delete '34' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
302
replace '302' by '6b'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
3
replace '3' by '66'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
3
replace '3' by '132'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
000415b2
replace '000415b2' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
980
replace '980' by '140'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
401
replace '401' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
4
replace '4' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
252
replace '252' by 'dceaf65b'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
4
replace '4' by '38'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
4
replace '4' by '33'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
24aael3a
replace '24aael3a' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
il
replace 'il' by '130'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
475
replace '475' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
297
replace '297' by 'clbe4071'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
4
replace '4' by '68'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
4
replace '4' by '123'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
fb15929f
replace 'fb15929f' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
85
replace '85' by '153'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
221
replace '221' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
236
replace '236' by '089dd312'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
4
replace '4' by 'it'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
4
replace '4' by '18'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
7c36f462
replace '7c36f462' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
29
replace '29' by '104'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
200
replace '200' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
200
replace '200' by '119c53eb'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
942be4f7
replace '942be4f7' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
HI
replace 'HI' by '124'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
380
replace '380' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
282
replace '282' by 'a74a7cb8'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
4
replace '4' by '108'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
4
replace '4' by '107'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
006b2fb6
replace '006b2fb6' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
lh
replace 'lh' by '431'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
1381
replace '1381' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
253
replace '253' by '89a3645c'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
15b86a25
replace '15b86a25' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
47
replace '47' by '130'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
436
replace '436' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
6
replace '6' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
144
replace '144' by '59955b4c'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
6
replace '6' by '38'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
6
replace '6' by '33'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
643116fF
replace '643116fF' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
33
replace '33' by '131'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
159
replace '159' by '2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
404
replace '404' by '65debcbe'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
6
replace '6' by 'il'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
6
replace '6' by '72'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
046dc081
replace '046dc081' by '643116fF'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
1
replace '1' by '33'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
331
replace '331' by '159'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
484
replace '484' by '404'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
aafff587
replace 'aafff587' by '046dc081'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
81
replace '81' by '1'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
781
replace '781' by '331'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
9
replace '9' by '6'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
461
replace '461' by '484'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
d2141ff2
replace 'd2141ff2' by 'aafff587'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
1
replace '1' by '81'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
726
replace '726' by '781'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
18
replace '18' by '9'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
310
replace '310' by '461'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
fbddlefa
replace 'fbddlefa' by 'd2141ff2'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
625
replace '625' by '726'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
635
replace '635' by '310'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
a32a6f4b
replace 'a32a6f4b' by 'fbddlefa'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
24
replace '24' by '1'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
697
replace '697' by '625'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
19
replace '19' by '18'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
401
replace '401' by '635'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
0d996462
replace '0d996462' by 'a32a6f4b'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
115
replace '115' by '24'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
446
replace '446' by '697'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
20
replace '20' by '19'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
355
replace '355' by '401'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
c11d82d
replace 'c11d82d' by '0d996462'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
92
replace '92' by '115'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
N/A
replace 'N/A' by '446'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
13.
replace '13.' by '20'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
N/A
replace 'N/A' by '355'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
21
replace '21' by '20'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
26
replace '26' by '20'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
8ebf4e44
replace '8ebf4e44' by 'c11d82d'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
419
replace '419' by '92'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
17
replace '17' by '13.'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
24
replace '24' by '21'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
30
replace '30' by '26'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
9d30e7f0
replace '9d30e7f0' by '8ebf4e44'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
100
replace '100' by '419'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
19
replace '19' by '17'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
26
delete '26' by ''
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
31
replace '31' by '24'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
47d29959
replace '47d29959' by '30'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
trans.
replace 'trans.' by '9d30e7f0'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
Mean
replace 'Mean' by '100'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
|
replace '|' by 'N/A'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
trans.
replace 'trans.' by '19'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
Mean
replace 'Mean' by 'N/A'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
evaded
replace 'evaded' by '26'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
Max.
replace 'Max.' by '31'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
|
replace '|' by '47d29959'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
insert '' by '|'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
insert '' by 'trans.'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
insert '' by 'Mean'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
div.
replace 'div.' by 'evaded'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
Feedback-guided
replace 'Feedback-guided' by 'Max.'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
insert '' by 'Feedback-guided'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
insert '' by 'div.'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
MINOS
replace 'MINOS' by 'Minos'
VirusTotal Minos [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b
VirusTotal MINOS [28] Hash #D Stochastic div. Feedback-guided div. Max. evaded Mean trans. | Max. evaded Mean trans. | Mean trans. 47d29959 31 26 N/A 19 N/A 100 9d30e7f0 30 24 N/A 17 N/A 419 8ebf4e44 26 21 N/A 13. N/A 92 c11d82d 20 20 355 20 446 115 0d996462 19 19 401 19 697 24 a32a6f4b 18 18 635 18 625 1 fbddlefa 18 18 310 18 726 1 d2141ff2 9 9 461 9 781 81 aafff587 6 6 484 6 331 1 046dc081 6 6 404 6 159 33 643116fF 6 6 144 6 436 47 15b86a25 4 4 253 4 1381 lh 006b2fb6 4 4 282 4 380 HI 942be4f7 4 4 200 4 200 29 7c36f462 4 4 236 4 221 85 fb15929f 4 4 297 4 475 il 24aael3a 4 4 252 4 401 980 000415b2 3 3 302 3 34 960 4cbdbbb1 3 3 295 3. 72 il 65debcbe 2 2 131 2 33 38 59955b4c 2 2 130 2 33 38 89a3645c 2 2 431 2 107 108 a74a7cb8 2 2 124 2 33 38 119c53eb 2 2 104 2 18 it 089dd312 2 2 153 2 123 68 clbe4071 2 2 130 2 33 38 dceaf65b 2 2 140 2 132 66 6b8c7899 2 2 143 2 33 38 a27b45ef 2 2 145 2 33 33 68ca7c0e 2 2 137 2 33 38 f0b24409 2 2 127 2 11 33 5bc53343 2 2 118 2 33 33 e09c32c5 1 1 120 1 488 15 Median 218 131 38
MINOS.
replace 'MINOS.' by 'Minos.'
Table 4.1: The table has two main categories for each malware oracle, corresponding to the two oracles we use: VirusTotal and Minos. For VirusTotal, divide the results based on the two diversification configurations: stochastic and feedback-guided diversification. We provide columns that indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which diversification setups are best, where the lower, the better. The MINOS section includes a column that specifies the number of transformations needed for complete evasion. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
Table 4.1: The table has two main categories for each malware oracle, corresponding to the two oracles we use: VirusTotal and MINOS. For VirusTotal, divide the results based on the two diversification configurations: stochastic and feedback-guided diversification. We provide columns that indicate the number of VirusTotal vendors that flag the original binary as malware (#D), the maximum number of successfully evaded detectors (Max. #evaded), and the average number of transformations required (Mean #trans.) for each sample. We highlight in bold text the values for which diversification setups are best, where the lower, the better. The MINOS section includes a column that specifies the number of transformations needed for complete evasion. The final row offers the median number of transformations required for evasion across our evaluated setups and oracles.
the
delete 'the' by ''
Feedback-guided diversification to evade VirusTotal: stochastic diversification does not guide the diversification based on the number of evaded detectors, it is purely random and has some drawbacks. For example, some transformations might suppress other transformations previously applied. We have observed that, by carefully selecting the order and type of transformations applied, it is possible to evade detection systems in fewer iterations. This can be appreciated in the results of the feedback-guided diversification part of Table 4.1. The feedback-guided diversification setup successfully generates variants that totally evade detection for 30 out of 33 binaries, it is thus as good as the stochastic setup. Remarkably, for 21 binaries out of 30, feedback-guided needs only 40% of the calls the stochastic diversification setup needs, demonstrating larger efficiency. Moreover, the lower number of transformations needed to evade detection, compared to the stochastic diversification setup, highlights the efficacy of the feedback-guided diversification setup in studying effective transformations. Consequently, malware detection system developers can leverage feedback-guided diversification to enhance their systems, focusing on identifying specific transformations.
Feedback-guided diversification to evade VirusTotal: stochastic diversification does not guide the diversification based on the number of evaded detectors, it is purely random and has some drawbacks. For example, some transformations might suppress other transformations previously applied. We have observed that, by carefully selecting the order and type of transformations applied, it is possible to evade detection systems in fewer iterations. This can be appreciated in the results of the feedback-guided diversification part of Table 4.1. The feedback-guided diversification setup successfully generates variants that totally evade the detection for 30 out of 33 binaries, it is thus as good as the stochastic setup. Remarkably, for 21 binaries out of 30, feedback-guided needs only 40% of the calls the stochastic diversification setup needs, demonstrating larger efficiency. Moreover, the lower number of transformations needed to evade detection, compared to the stochastic diversification setup, highlights the efficacy of the feedback-guided diversification setup in studying effective transformations. Consequently, malware detection system developers can leverage feedback-guided diversification to enhance their systems, focusing on identifying specific transformations.
2.1.5
delete '2.1.5' by ''
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Section
delete 'Section' by ''
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
In
delete 'In' by ''
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
VirusTotal.
delete 'VirusTotal.' by ''
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
within
delete 'within' by ''
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
vendors
replace 'vendors' by '2.1.5,'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
general-purpose
replace 'general-purpose' by 'Section'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
the
replace 'the' by 'In'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
from
replace 'from' by 'VirusTotal.'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
differ
replace 'differ' by 'within'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
which
replace 'which' by 'vendors'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
WebAssembly,
replace 'WebAssembly,' by 'general-purpose'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
for
replace 'for' by 'the'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
solutions
replace 'solutions' by 'from'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
specialized
replace 'specialized' by 'differ'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
of
replace 'of' by 'which'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
existence
replace 'existence' by 'WebAssembly,'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
the
replace 'the' by 'for'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
given
replace 'given' by 'solutions'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
particularly
replace 'particularly' by 'specialized'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
issues,
replace 'issues,' by 'of'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
pose
replace 'pose' by 'existence'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
could
replace 'could' by 'the'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
detection
replace 'detection' by 'given'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
for
replace 'for' by 'particularly'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
on
replace 'on' by 'pose'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
exclusively
replace 'exclusively' by 'could'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Relying
replace 'Relying' by 'detection'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
MINOS:
replace 'MINOS:' by 'for'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
evade
replace 'evade' by 'VirusTotal'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
to
replace 'to' by 'on'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
diversification
replace 'diversification' by 'exclusively'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic
replace 'Stochastic' by 'Relying'
Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5, we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
Stochastic diversification to evade MINOS: Relying exclusively on VirusTotal for detection could pose issues, particularly given the existence of specialized solutions for WebAssembly, which differ from the general-purpose vendors within VirusTotal. In Section 2.1.5 we highlight several examples of such solutions. Yet, for its simplicity, we extend this experiment by using MINOS [28], an antivirus specifically designed for WebAssembly. The results of evading MINOS can be seen in the final column of Table 4.1. The bottom row of Table 4.1 highlights that fewer iterations are required to evade MINOS than VirusTotal through WebAssembly diversification, indicating a greater ease in eluding MINOS. The stochastic diversification setup requires a median iteration count of 218 to evade VirusTotal. In contrast, the feedback-guided diversification setup necessitates only 131 iterations. Remarkably, a mere 38 iterations are needed for MINOS. WASM-MUTATE evaded detection for 8 out of 33 binaries in a single iteration. This result implies the susceptibility of the MINOS model to binary diversification.
correctness:
delete 'correctness:' by ''
To evaluate the correctness of the malware variants created with WASM-MUTATE, we focused on six binaries that we could build and execute end-to-end, as these had all three components outlined in Figure 4.1. We select only six binaries because the process of building and executing the binaries involves three components: the WebAssembly binary, its JavaScript complement, and the miner pool. These components were not found for the remaining 24 evaded binaries in the study subjects. For the six binaries, we then replace the original WebAssembly code with variants generated using VirusTotal as the malware oracle and WASM-MUTATE for both controlled and stochastic diversification configurations. We then execute both the original and the generated variants. We assess the correctness of the variants by examining the hashes they generate. Our findings show that all variants generated with WASM-MUTATE are correct, i.e., they generate the correct hashes and execute without error. Additionally, we found that 19% of the generated variants surpassed the original cryptojacking binaries in performance.
WebAssembly variants correctness: To evaluate the correctness of the malware variants created with WASM-MUTATE, we focused on six binaries that we could build and execute end-to-end, as these had all three components outlined in Figure 4.1. We select only six binaries because the process of building and executing the binaries involves three components: the WebAssembly binary, its JavaScript complement, and the miner pool. These components were not found for the remaining 24 evaded binaries in the study subjects. For the six binaries, we then replace the original WebAssembly code with variants generated using VirusTotal as the malware oracle and WASM-MUTATE for both controlled and stochastic diversification configurations. We then execute both the original and the generated variants. We assess the correctness of the variants by examining the hashes they generate. Our findings show that all variants generated with WASM-MUTATE are correct, i.e., they generate the correct hashes and execute without error. Additionally, we found that 19% of the generated variants surpassed the original cryptojacking binaries in performance.
variants
delete 'variants' by ''
To evaluate the correctness of the malware variants created with WASM-MUTATE, we focused on six binaries that we could build and execute end-to-end, as these had all three components outlined in Figure 4.1. We select only six binaries because the process of building and executing the binaries involves three components: the WebAssembly binary, its JavaScript complement, and the miner pool. These components were not found for the remaining 24 evaded binaries in the study subjects. For the six binaries, we then replace the original WebAssembly code with variants generated using VirusTotal as the malware oracle and WASM-MUTATE for both controlled and stochastic diversification configurations. We then execute both the original and the generated variants. We assess the correctness of the variants by examining the hashes they generate. Our findings show that all variants generated with WASM-MUTATE are correct, i.e., they generate the correct hashes and execute without error. Additionally, we found that 19% of the generated variants surpassed the original cryptojacking binaries in performance.
WebAssembly variants correctness: To evaluate the correctness of the malware variants created with WASM-MUTATE, we focused on six binaries that we could build and execute end-to-end, as these had all three components outlined in Figure 4.1. We select only six binaries because the process of building and executing the binaries involves three components: the WebAssembly binary, its JavaScript complement, and the miner pool. These components were not found for the remaining 24 evaded binaries in the study subjects. For the six binaries, we then replace the original WebAssembly code with variants generated using VirusTotal as the malware oracle and WASM-MUTATE for both controlled and stochastic diversification configurations. We then execute both the original and the generated variants. We assess the correctness of the variants by examining the hashes they generate. Our findings show that all variants generated with WASM-MUTATE are correct, i.e., they generate the correct hashes and execute without error. Additionally, we found that 19% of the generated variants surpassed the original cryptojacking binaries in performance.
protection
replace 'protection' by 'protection.'
Defensive Diversification: speculative side-channel protection.
Defensive Diversification: speculative side-channel protection
the
delete 'the' by ''
Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating variants generated by WASM-MUTATE.
Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata (WebAssembly custom sections) obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating the variants generated by WASM-MUTATE.
sections)
delete 'sections)' by ''
Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating variants generated by WASM-MUTATE.
Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata (WebAssembly custom sections) obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating the variants generated by WASM-MUTATE.
custom
delete 'custom' by ''
Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating variants generated by WASM-MUTATE.
Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata (WebAssembly custom sections) obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating the variants generated by WASM-MUTATE.
(WebAssembly
delete '(WebAssembly' by ''
Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating variants generated by WASM-MUTATE.
Reflection Malware detection presents a challenging and well-known issue [150]. While there are considerable efforts on preventing malware in WebAssembly, the current literature acknowledges only metadata (WebAssembly custom sections) obfuscation or total absence of obfuscation techniques for WebAssembly [26, 27, 29, 30, 28]. As explored in Section 2.2, a software diversification engine could potentially serve as an obfuscator. We exhibit this potential with WASM-MUTATE. Moreover, our software diversification tools offer a feasible method to improve the precision of WebAssembly malware detection systems. Existing tools could enhance their evaluation dataset of WebAssembly malware by incorporating the variants generated by WASM-MUTATE.
4.3).
replace '4.3).' by '4.3.'
The fundamental advantage of using WebAssembly in FaaS platforms lies in its ability to encapsulate thousands of WebAssembly binaries within a singular host process. A developer could compile its source code into a WebAssembly program suitable for the cloud platform and then submit it (in Figure 4.3). This host process is then disseminated across a network of servers and data centers (2) in Figure 4.3. These platforms convert WebAssembly programs into native code, which is subsequently executed in a sandboxed environment. Host processes can then instantiate new WebAssembly sandboxes for each client function, executing them in response to specific user requests with nanosecond-level latency (@) in Figure 4.3. This architecture inherently isolates WebAssembly binary executions from each other as well as from the host process, enhancing security.
The fundamental advantage of using WebAssembly in FaaS platforms lies in its ability to encapsulate thousands of WebAssembly binaries within a singular host process. A developer could compile its source code into a WebAssembly program suitable for the cloud platform and then submit it (in Figure 4.3). This host process is then disseminated across a network of servers and data centers (2) in Figure 4.3). These platforms convert WebAssembly programs into native code, which is subsequently executed in a sandboxed environment. Host processes can then instantiate new WebAssembly sandboxes for each client function, executing them in response to specific user requests with nanosecond-level latency (@) in Figure 4.3). This architecture inherently isolates WebAssembly binary executions from each other as well as from the host process, enhancing security.
\
delete '\' by ''
Source Code Developer Computer Upload — HTTP request Memory isolation is violated | Memory boundary Host , Cloud node process 1 1 I NSS | i @) X86 1 1 1 !
Source Code Developer Computer Upload — HTTP request Memory isolation is violated | Memory boundary Host , Cloud node process 1 1 I NSS | i @) X86 1 ' 1 1 ! \
'
delete ''' by ''
Source Code Developer Computer Upload — HTTP request Memory isolation is violated | Memory boundary Host , Cloud node process 1 1 I NSS | i @) X86 1 1 1 !
Source Code Developer Computer Upload — HTTP request Memory isolation is violated | Memory boundary Host , Cloud node process 1 1 I NSS | i @) X86 1 ' 1 1 ! \
,
Put a space after the comma, but not before the comma.
Source Code Developer Computer Upload — HTTP request Memory isolation is violated | Memory boundary Host , Cloud node process 1 1 I NSS | i @) X86 1 ' 1 1 ! \
4.3).
replace '4.3).' by '4.3.'
However, while WebAssembly is engineered with a strong focus on security and isolation, it is not entirely immune to vulnerabilities such as Spectre attacks [151, 142] () in Figure 4.3. In the sections that follow, we explore how software diversification techniques can be employed to harden WebAssembly binaries against such attacks.
However, while WebAssembly is engineered with a strong focus on security and isolation, it is not entirely immune to vulnerabilities such as Spectre attacks [151, 142] () in Figure 4.3). In the sections that follow, we explore how software diversification techniques can be employed to harden WebAssembly binaries against such attacks.
attacks
replace 'attacks' by 'attacks.'
Threat model: speculative side-channel attacks.
Threat model: speculative side-channel attacks
process
replace 'process' by 'process.'
Source Code Software Diversification Developer Computer request HTTP Memory isolation M bound. is violated MEY OUNCE, X86 Host _ Cloud node process.
Source Code Software Diversification Developer Computer request HTTP Memory isolation M bound. is violated MEY OUNCE, X86 Host _ Cloud node process
in
replace 'in' by 'In'
To empirically demonstrate that Software Diversification can indeed mitigate Spectre vulnerabilities, we reuse the WebAssembly attack scenarios proposed by Narayan et al. In their work on Swivel [63]. Swivel is a compiler-based strategy designed to counteract Spectre attacks on WebAssembly binaries by linearizing their control flow during machine code compilation. Our approach differs from theirs in that it is binary-based, compiler-agnostic, and platform-agnostic; we do not propose altering the deployment or toolchain of FaaS platforms.
To empirically demonstrate that Software Diversification can indeed mitigate Spectre vulnerabilities, we reuse the WebAssembly attack scenarios proposed by Narayan et al. in their work on Swivel [63]. Swivel is a compiler-based strategy designed to counteract Spectre attacks on WebAssembly binaries by linearizing their control flow during machine code compilation. Our approach differs from theirs in that it is binary-based, compiler-agnostic, and platform-agnostic; we do not propose altering the deployment or toolchain of FaaS platforms.
insert '' by 'Method'
Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Method
Methodology
insert '' by 'Methodology'
Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Methodology Method
Methodology
Results
replace 'Results' by 'Results:'
Results:
Results
attacks
replace 'attacks' by 'attacks,'
The previous metric not only captures the success or failure of the attacks, but also quantifies the extent to which data exfiltration is hindered. For example, a variant that still leaks data but does so at an impractically slow rate would be considered hardened against the attack.
The previous metric not only captures the success or failure of the attacks but also quantifies the extent to which data exfiltration is hindered. For example, a variant that still leaks data but does so at an impractically slow rate would be considered hardened against the attack.
—
delete '—' by ''
Definition 8 (Attack bandwidth) Given data D = bo, 01,...,bc being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ———————.
Definition 8 (Attack bandwidth) Given data D = {bo, 01,...,bc} being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ——— —
———
replace '———' by '———————.'
Definition 8 (Attack bandwidth) Given data D = bo, 01,...,bc being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ———————.
Definition 8 (Attack bandwidth) Given data D = {bo, 01,...,bc} being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ——— —
01,...,bc}
replace '01,...,bc}' by '01,...,bc'
Definition 8 (Attack bandwidth) Given data D = bo, 01,...,bc being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ———————.
Definition 8 (Attack bandwidth) Given data D = {bo, 01,...,bc} being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ——— —
{bo,
replace '{bo,' by 'bo,'
Definition 8 (Attack bandwidth) Given data D = bo, 01,...,bc being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ———————.
Definition 8 (Attack bandwidth) Given data D = {bo, 01,...,bc} being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ——— —
,bc
Put a space after the comma.
Definition 8 (Attack bandwidth) Given data D = {bo, 01,...,bc} being ezfiltrated in time T and K = ko,ki,...,kn the collection of correct data bytes, the bandwidth metric is defined as: — ——— —
(pht)
replace '(pht)' by '(pht).'
Program Attack btb_breakout | Spectre branch target buffer (btb) btb_leakage | Spectre branch target buffer (btb) ret2spec Spectre Return Stack Buffer (rsb sb) pht Spectre Pattern History Table (pht).
Program Attack btb_breakout | Spectre branch target buffer (btb) btb_leakage | Spectre branch target buffer (btb) ret2spec Spectre Return Stack Buffer (rsb sb) pht Spectre Pattern History Table (pht)
transformations
delete 'transformations' by ''
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
Stacked
delete 'Stacked' by ''
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
Wpeapueg,
delete 'Wpeapueg,' by ''
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
(8/4)
delete '(8/4)' by ''
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
8
delete '8' by ''
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
2
delete '2' by ''
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
8
replace '8' by 'Wapeapueg,'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
ge
replace 'ge' by '(8/4)'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
—
replace '—' by '8'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
*
replace '*' by '2'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
I
replace 'I' by '8'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
———
replace '———' by 'ge'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
EN
replace 'EN' by '—'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
ee
replace 'ee' by '*'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
SSL
replace 'SSL' by 'I'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
a,
replace 'a,' by '———'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
*s,,
replace '*s,,' by 'SSL'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
ee
replace 'ee' by 'a,'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
*,
replace '*,' by '*s,,'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
a
replace 'a' by 'ee'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
%
replace '%' by '*,'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
Se,
replace 'Se,' by 'a'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
=
replace '=' by '%'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
*,
replace '*,' by 'Se,'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
=e
replace '=e' by '='
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
1
replace '1' by '=e'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
*:
replace '*:' by '%'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
[>
replace '[>' by '1'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
*
replace '*' by '*:'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
I
replace 'I' by '[>'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
a
replace 'a' by '*'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
0.0
replace '0.0' by 'I'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
8
replace '8' by 'a'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
1.0
replace '1.0' by '0.0'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
Wapeapueg
replace 'Wapeapueg' by '8'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
(8/4)
replace '(8/4)' by '1.0'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
Ss
replace 'Ss' by 'Wapeapueg'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
Ss
replace 'Ss' by '(8/4)'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
os
replace 'os' by 'Ss'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
2
replace '2' by 'Ss'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
2
replace '2' by 'os'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
4
replace '4' by '2'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
‘HM
replace '‘HM' by '2'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
Ss
replace 'Ss' by '4'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
=
replace '=' by '‘HM'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
&
replace '&' by 'Ss'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
s
replace 's' by '='
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
*
replace '*' by '&'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
=
replace '=' by 's'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
SF
replace 'SF' by '*'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
SF
replace 'SF' by '='
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
Ss
replace 'Ss' by 'SF'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
insert '' by 'Ss'
pht ret2spec s Ss Ss SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ——— I * — ge 8 2 8 (8/4) Wapeapueg, Stacked
pht ret2spec s Ss Ss SF SF = * s & = Ss ‘HM 4 2 2 os Ss Ss Ss (8/4) Wapeapueg 1.0 8 0.0 a I * [> *: 1 % =e *, = Se, % a *, ee *s,, a, SSL ee EN ——— I * — ge 8 2 8 (8/4) Wpeapueg, Stacked transformations Stacked transformations
Strength:
delete 'Strength:' by ''
For the binaries btb_breakout and btb_leakage, WASM-MUTATE exhibits a high level of effectiveness, generating variants that leak less information than the original in 78% and 70% of instances, respectively. For both programs, after applying 200 stacked transformations, the exfiltration bandwidth drops to zero. This implies that WASM-MUTATE is capable of synthesizing variants that are entirely protected from the original attack. If we consider the results in Table 3.1, generating a variant with 200 stacked transformations can be accomplished in just a matter of seconds for a single WebAssembly binary.
Population Strength: For the binaries btb_breakout and btb_leakage, WASM-MUTATE exhibits a high level of effectiveness, generating variants that leak less information than the original in 78% and 70% of instances, respectively. For both programs, after applying 200 stacked transformations, the exfiltration bandwidth drops to zero. This implies that WASM-MUTATE is capable of synthesizing variants that are entirely protected from the original attack. If we consider the results in Table 3.1, generating a variant with 200 stacked transformations can be accomplished in just a matter of seconds for a single WebAssembly binary.
Population
delete 'Population' by ''
For the binaries btb_breakout and btb_leakage, WASM-MUTATE exhibits a high level of effectiveness, generating variants that leak less information than the original in 78% and 70% of instances, respectively. For both programs, after applying 200 stacked transformations, the exfiltration bandwidth drops to zero. This implies that WASM-MUTATE is capable of synthesizing variants that are entirely protected from the original attack. If we consider the results in Table 3.1, generating a variant with 200 stacked transformations can be accomplished in just a matter of seconds for a single WebAssembly binary.
Population Strength: For the binaries btb_breakout and btb_leakage, WASM-MUTATE exhibits a high level of effectiveness, generating variants that leak less information than the original in 78% and 70% of instances, respectively. For both programs, after applying 200 stacked transformations, the exfiltration bandwidth drops to zero. This implies that WASM-MUTATE is capable of synthesizing variants that are entirely protected from the original attack. If we consider the results in Table 3.1, generating a variant with 200 stacked transformations can be accomplished in just a matter of seconds for a single WebAssembly binary.
transformations
delete 'transformations' by ''
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
Stacked
delete 'Stacked' by ''
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
oS
delete 'oS' by ''
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
PF
delete 'PF' by ''
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
FFF
delete 'FFF' by ''
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
SMe
replace 'SMe' by 'transformations.'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
S
replace 'S' by 'Stacked'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
LSS
replace 'LSS' by 'transformations'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
FFF
replace 'FFF' by 'Stacked'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
SF
replace 'SF' by 'oS'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
S
replace 'S' by 'PF'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
SF
replace 'SF' by 'FFF'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
©
replace '©' by 'SMe'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
FF
replace 'FF' by 'S'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
Ss
replace 'Ss' by 'LSS'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
se
replace 'se' by 'FFF'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
Ss
replace 'Ss' by 'SF'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
s
replace 's' by 'S'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
SF
delete 'SF' by ''
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
-=
delete '-=' by ''
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
insert '' by 'Ss'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
insert '' by 'FF'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
El)
replace 'El)' by 'SF'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
Weve.
replace 'Weve.' by 'Ss'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
a
replace 'a' by '-='
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
8
replace '8' by 's'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
a
replace 'a' by 'El)'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
S
replace 'S' by 'Weve.'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
(b/s)
replace '(b/s)' by 'a'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
Bandwidth
replace 'Bandwidth' by '8'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
So
replace 'So' by 'a'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
insert '' by '(b/s)'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
insert '' by 'Bandwidth'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
on
replace 'on' by 'So'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
insert '' by 'on'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
uw
replace 'uw' by 'S'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
>
replace '>' by 'uw'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
Nn
replace 'Nn' by '>'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
~
replace '~' by 'Nn'
N a Bandwidth (b/s) _ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss FF SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations.
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
S S
Possible typo: you repeated a word
N a Bandwidth (b/s) _ ~ Nn > uw S S on S So Bandwidth (b/s) S a 8 a Weve. El) s -= Ss SF FF Ss s Ss se Ss FF © SF S SF FFF LSS S SMe FFF PF oS Stacked transformations Stacked transformations
insert '' by 'it.'
Then there is the question of whether or not there is a need for it.
insert '' by 'for'
Then there is the question of whether or not there is a need for it.
insert '' by 'need'
Then there is the question of whether or not there is a need for it.
insert '' by 'a'
Then there is the question of whether or not there is a need for it.
insert '' by 'is'
Then there is the question of whether or not there is a need for it.
insert '' by 'there'
Then there is the question of whether or not there is a need for it.
insert '' by 'not'
Then there is the question of whether or not there is a need for it.
insert '' by 'or'
Then there is the question of whether or not there is a need for it.
insert '' by 'whether'
Then there is the question of whether or not there is a need for it.
insert '' by 'of'
Then there is the question of whether or not there is a need for it.
insert '' by 'question'
Then there is the question of whether or not there is a need for it.
insert '' by 'the'
Then there is the question of whether or not there is a need for it.
insert '' by 'Then'
Then there is the question of whether or not there is a need for it.
programs
replace 'programs' by 'programs,'
Figure 4.6: Impact of WASM-MUTATE over ret2spec and pht binaries. The Y-axis denotes eaxfiltration bandwidth, with the original binary’s bandwidth under attack highlighted by a blue marker and dashed line. Variants are clustered in groups of 100 stacked transformations, denoted by green violinplots. Overall, for both programs, approximately 70% of the variants have less data leakage bandwidth.
Figure 4.6: Impact of WASM-MUTATE over ret2spec and pht binaries. The Y-axis denotes eaxfiltration bandwidth, with the original binary’s bandwidth under attack highlighted by a blue marker and dashed line. Variants are clustered in groups of 100 stacked transformations, denoted by green violinplots. Overall, for both programs approximately 70% of the variants have less data leakage bandwidth.
btb_breakout
replace 'btb_breakout' by 'btb_breakout.'
Code from original btb_breakout.
Code from original btb_breakout
insert '' by 'cache'
Disrupting timers to measure cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.
Disrupting timers: Cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.
insert '' by 'measure'
Disrupting timers to measure cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.
Disrupting timers: Cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.
Cache
replace 'Cache' by 'to'
Disrupting timers to measure cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.
Disrupting timers: Cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.
timers:
replace 'timers:' by 'timers'
Disrupting timers to measure cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.
Disrupting timers: Cache timing side-channel attacks, including for the four binaries analyzed in this use case, depend on precise timers to measure cache access times. Disrupting these timers can effectively neutralize the attack [152]. One key reason our results show variants resilient to Spectre attacks is the approach of WASM-MUTATE. It creates variants that offer a similar approach. Our WebAssembly variants introduce perturbations in the timing steps of WebAssembly variants. This is illustrated in Listing 4.1 and Listing 4.2, where the former shows the original time measurement and the latter presents a variant with introduced operations. By introducing additional instructions, the inherent randomness in the time measurement of a single or a few instructions is amplified, thereby reducing the timer’s accuracy.
WASM-MUTATE:
delete 'WASM-MUTATE:' by ''
As illustrated in Figure 4.6, similarly to Figure 4.5, WASM-MUTATE significantly impacts the programs ret2spec and pht when subjected to their respective attacks. In 76% of instances for ret2spec and 71% for pht, the generated variants demonstrated reduced attack bandwidth compared to the original binaries. The plots reveal that a notable decrease in exfiltration bandwidth occurs after applying at least 100 stacked transformations. While both programs show signs of hardening through reduced attack bandwidth, this effect is not immediate and requires a substantial number of transformations to become effective. Additionally, the bandwidth distribution is more varied for these two programs compared to the two previous ones. Our analysis suggests a correlation between the reduction in attack bandwidth and the complexity of the binary being diversified. Specifically, ret2spec and pht are substantially larger programs, containing over 300,000 instructions, compared to btb_breakout and btb_leakage, which have fewer than 800 instructions. Therefore, given that WASM-MUTATE performs one transformation per invocation, the probability of affecting critical components to hinder attacks decreases in larger binaries.
Effectiveness of WASM-MUTATE: As illustrated in Figure 4.6, similarly to Figure 4.5, WASM-MUTATE significantly impacts the programs ret2spec and pht when subjected to their respective attacks. In 76% of instances for ret2spec and 71% for pht, the generated variants demonstrated reduced attack bandwidth compared to the original binaries. The plots reveal that a notable decrease in exfiltration bandwidth occurs after applying at least 100 stacked transformations. While both programs show signs of hardening through reduced attack bandwidth, this effect is not immediate and requires a substantial number of transformations to become effective. Additionally, the bandwidth distribution is more varied for these two programs compared to the two previous ones. Our analysis suggests a correlation between the reduction in attack bandwidth and the complexity of the binary being diversified. Specifically, ret2spec and pht are substantially larger programs, containing over 300,000 instructions, compared to btb_breakout and btb_leakage, which have fewer than 800 instructions. Therefore, given that WASM-MUTATE performs one transformation per invocation, the probability of affecting critical components to hinder attacks decreases in larger binaries.
of
delete 'of' by ''
As illustrated in Figure 4.6, similarly to Figure 4.5, WASM-MUTATE significantly impacts the programs ret2spec and pht when subjected to their respective attacks. In 76% of instances for ret2spec and 71% for pht, the generated variants demonstrated reduced attack bandwidth compared to the original binaries. The plots reveal that a notable decrease in exfiltration bandwidth occurs after applying at least 100 stacked transformations. While both programs show signs of hardening through reduced attack bandwidth, this effect is not immediate and requires a substantial number of transformations to become effective. Additionally, the bandwidth distribution is more varied for these two programs compared to the two previous ones. Our analysis suggests a correlation between the reduction in attack bandwidth and the complexity of the binary being diversified. Specifically, ret2spec and pht are substantially larger programs, containing over 300,000 instructions, compared to btb_breakout and btb_leakage, which have fewer than 800 instructions. Therefore, given that WASM-MUTATE performs one transformation per invocation, the probability of affecting critical components to hinder attacks decreases in larger binaries.
Effectiveness of WASM-MUTATE: As illustrated in Figure 4.6, similarly to Figure 4.5, WASM-MUTATE significantly impacts the programs ret2spec and pht when subjected to their respective attacks. In 76% of instances for ret2spec and 71% for pht, the generated variants demonstrated reduced attack bandwidth compared to the original binaries. The plots reveal that a notable decrease in exfiltration bandwidth occurs after applying at least 100 stacked transformations. While both programs show signs of hardening through reduced attack bandwidth, this effect is not immediate and requires a substantial number of transformations to become effective. Additionally, the bandwidth distribution is more varied for these two programs compared to the two previous ones. Our analysis suggests a correlation between the reduction in attack bandwidth and the complexity of the binary being diversified. Specifically, ret2spec and pht are substantially larger programs, containing over 300,000 instructions, compared to btb_breakout and btb_leakage, which have fewer than 800 instructions. Therefore, given that WASM-MUTATE performs one transformation per invocation, the probability of affecting critical components to hinder attacks decreases in larger binaries.
Effectiveness
delete 'Effectiveness' by ''
As illustrated in Figure 4.6, similarly to Figure 4.5, WASM-MUTATE significantly impacts the programs ret2spec and pht when subjected to their respective attacks. In 76% of instances for ret2spec and 71% for pht, the generated variants demonstrated reduced attack bandwidth compared to the original binaries. The plots reveal that a notable decrease in exfiltration bandwidth occurs after applying at least 100 stacked transformations. While both programs show signs of hardening through reduced attack bandwidth, this effect is not immediate and requires a substantial number of transformations to become effective. Additionally, the bandwidth distribution is more varied for these two programs compared to the two previous ones. Our analysis suggests a correlation between the reduction in attack bandwidth and the complexity of the binary being diversified. Specifically, ret2spec and pht are substantially larger programs, containing over 300,000 instructions, compared to btb_breakout and btb_leakage, which have fewer than 800 instructions. Therefore, given that WASM-MUTATE performs one transformation per invocation, the probability of affecting critical components to hinder attacks decreases in larger binaries.
Effectiveness of WASM-MUTATE: As illustrated in Figure 4.6, similarly to Figure 4.5, WASM-MUTATE significantly impacts the programs ret2spec and pht when subjected to their respective attacks. In 76% of instances for ret2spec and 71% for pht, the generated variants demonstrated reduced attack bandwidth compared to the original binaries. The plots reveal that a notable decrease in exfiltration bandwidth occurs after applying at least 100 stacked transformations. While both programs show signs of hardening through reduced attack bandwidth, this effect is not immediate and requires a substantial number of transformations to become effective. Additionally, the bandwidth distribution is more varied for these two programs compared to the two previous ones. Our analysis suggests a correlation between the reduction in attack bandwidth and the complexity of the binary being diversified. Specifically, ret2spec and pht are substantially larger programs, containing over 300,000 instructions, compared to btb_breakout and btb_leakage, which have fewer than 800 instructions. Therefore, given that WASM-MUTATE performs one transformation per invocation, the probability of affecting critical components to hinder attacks decreases in larger binaries.
optimization
replace 'optimization' by 'optimization.'
Train the code to jump here (index 1) (132.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization.
train the code to jump here (index 1) (132.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization
train
replace 'train' by 'Train'
Train the code to jump here (index 1) (132.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization.
train the code to jump here (index 1) (132.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization
train
This sentence does not start with an uppercase letter.
train the code to jump here (index 1) (132.load (i32.const 2000)) (i32.store (i32.const 83)) just prevent optimization
replace ' (call $readTimer) (set_local $end_time) inserted instructions> access to mem inserted instructions> (i64.sub (get_local $end_time) (get_local $start_time)) (set_local $duration)
(call $readTimer) (set_local $end_time) Happy suggestion
Suggestions
code
replace 'code' by 'code.'
Variant code.
Variant code
transiently
replace 'transiently' by 'Transiently'
Transiently jump here (132.load (i32.const 339968)) S(83) is the secret (i32.store (i32.const 83)) just prevent optimization Listing 4.3: Two jump locations. The top one trains the branch predictor, the bottom one is the expected jump that exfiltrates the memory access.
transiently jump here (132.load (i32.const 339968)) S(83) is the secret (i32.store (i32.const 83)) just prevent optimization Listing 4.3: Two jump locations. The top one trains the branch predictor, the bottom one is the expected jump that exfiltrates the memory access.
transiently
This sentence does not start with an uppercase letter.
transiently jump here (132.load (i32.const 339968)) S(83) is the secret (i32.store (i32.const 83)) just prevent optimization Listing 4.3: Two jump locations. The top one trains the branch predictor, the bottom one is the expected jump that exfiltrates the memory access.
4.4:
replace '4.4:' by '4.4'
Listing 4.4 WebAssembly variant with more instructions added indindinctly between jump places.
Listing 4.4: WebAssembly variant with more instructions added indindinctly between jump places.
optimization
replace 'optimization' by 'optimization.'
Transiently jump here inserted instructions> (i32.load (i32.const 339968)) inserted instructions> (i32.store (i32.const 83)) "S"(83) is the secret just prevent optimization.
transiently jump here
replace ' Transiently jump here inserted instructions> (i32.load (i32.const 339968)) inserted instructions> (i32.store (i32.const 83)) "S"(83) is the secret just prevent optimization.
transiently jump here Happy suggestion
Suggestions
transiently
replace 'transiently' by 'Transiently'
Transiently jump here inserted instructions> (i32.load (i32.const 339968)) inserted instructions> (i32.store (i32.const 83)) "S"(83) is the secret just prevent optimization.
transiently jump here
transiently
This sentence does not start with an uppercase letter.
transiently jump here
optimization
replace 'optimization' by 'optimization.'
Train the code to jump here (index 1) inserted instructions> (i32.load (i32.const 2000)) inserted instructions> (i32.store (i32.const 83)) just prevent optimization.
train the code to jump here (index 1)
replace ' Train the code to jump here (index 1) inserted instructions> (i32.load (i32.const 2000)) inserted instructions> (i32.store (i32.const 83)) just prevent optimization.
train the code to jump here (index 1) Happy suggestion
Suggestions
train
replace 'train' by 'Train'
Train the code to jump here (index 1) inserted instructions> (i32.load (i32.const 2000)) inserted instructions> (i32.store (i32.const 83)) just prevent optimization.
train the code to jump here (index 1)
train
This sentence does not start with an uppercase letter.
train the code to jump here (index 1)
impact:
delete 'impact:' by ''
The success in diminishing Spectre attacks is mainly explained by the fact that WASM-MUTATE synthesizes variants that effectively alter memory access patterns. We have identified four primary factors responsible for the divergence in memory accesses among WASM-MUTATE generated variants. First, modifications to the binary layout—even those that do not affect executed code—inevitably alter memory accesses within the program’s stack. Specifically, WASM-MUTATE generates variants that modify the return addresses of functions, which consequently leads to differences in execution flow and memory accesses. Second, one of our rewriting rules incorporates artificial global values into WebAssembly binaries. The access to these global variables inevitably affects the managed memory (see Section 2.1.3). Third, WASM-MUTATE injects ‘phantom’ instructions which do not aim to modify the outcome of a transformed function during execution. These intermediate calculations trigger the spill/reload component of the wasmtime compiler, varying spill and reload operations. In the context of limited physical resources, these operations temporarily store values in memory for later retrieval and use, thus creating diverse managed memory accesses (see the example at Section 3.3.1). Finally, certain rewriting rules implemented by WASM-MUTATE replicate fragments of code, e.g., performing commutative operations. These code segments may contain memory accesses, and while neither the memory addresses nor their values change, the frequency of these operations does.
Managed memory impact: The success in diminishing Spectre attacks is mainly explained by the fact that WASM-MUTATE synthesizes variants that effectively alter memory access patterns. We have identified four primary factors responsible for the divergence in memory accesses among WASM-MUTATE generated variants. First, modifications to the binary layout—even those that do not affect executed code—inevitably alter memory accesses within the program’s stack. Specifically, WASM-MUTATE generates variants that modify the return addresses of functions, which consequently leads to differences in execution flow and memory accesses. Second, one of our rewriting rules incorporates artificial global values into WebAssembly binaries. The access to these global variables inevitably affects the managed memory (see Section 2.1.3). Third, WASM-MUTATE injects ‘phantom’ instructions which do not aim to modify the outcome of a transformed function during execution. These intermediate calculations trigger the spill/reload component of the wasmtime compiler, varying spill and reload operations. In the context of limited physical resources, these operations temporarily store values in memory for later retrieval and use, thus creating diverse managed memory accesses (see the example at Section 3.3.1). Finally, certain rewriting rules implemented by WASM-MUTATE replicate fragments of code, e.g., performing commutative operations. These code segments may contain memory accesses, and while neither the memory addresses nor their values change, the frequency of these operations does.
memory
delete 'memory' by ''
The success in diminishing Spectre attacks is mainly explained by the fact that WASM-MUTATE synthesizes variants that effectively alter memory access patterns. We have identified four primary factors responsible for the divergence in memory accesses among WASM-MUTATE generated variants. First, modifications to the binary layout—even those that do not affect executed code—inevitably alter memory accesses within the program’s stack. Specifically, WASM-MUTATE generates variants that modify the return addresses of functions, which consequently leads to differences in execution flow and memory accesses. Second, one of our rewriting rules incorporates artificial global values into WebAssembly binaries. The access to these global variables inevitably affects the managed memory (see Section 2.1.3). Third, WASM-MUTATE injects ‘phantom’ instructions which do not aim to modify the outcome of a transformed function during execution. These intermediate calculations trigger the spill/reload component of the wasmtime compiler, varying spill and reload operations. In the context of limited physical resources, these operations temporarily store values in memory for later retrieval and use, thus creating diverse managed memory accesses (see the example at Section 3.3.1). Finally, certain rewriting rules implemented by WASM-MUTATE replicate fragments of code, e.g., performing commutative operations. These code segments may contain memory accesses, and while neither the memory addresses nor their values change, the frequency of these operations does.
Managed memory impact: The success in diminishing Spectre attacks is mainly explained by the fact that WASM-MUTATE synthesizes variants that effectively alter memory access patterns. We have identified four primary factors responsible for the divergence in memory accesses among WASM-MUTATE generated variants. First, modifications to the binary layout—even those that do not affect executed code—inevitably alter memory accesses within the program’s stack. Specifically, WASM-MUTATE generates variants that modify the return addresses of functions, which consequently leads to differences in execution flow and memory accesses. Second, one of our rewriting rules incorporates artificial global values into WebAssembly binaries. The access to these global variables inevitably affects the managed memory (see Section 2.1.3). Third, WASM-MUTATE injects ‘phantom’ instructions which do not aim to modify the outcome of a transformed function during execution. These intermediate calculations trigger the spill/reload component of the wasmtime compiler, varying spill and reload operations. In the context of limited physical resources, these operations temporarily store values in memory for later retrieval and use, thus creating diverse managed memory accesses (see the example at Section 3.3.1). Finally, certain rewriting rules implemented by WASM-MUTATE replicate fragments of code, e.g., performing commutative operations. These code segments may contain memory accesses, and while neither the memory addresses nor their values change, the frequency of these operations does.
Managed
delete 'Managed' by ''
The success in diminishing Spectre attacks is mainly explained by the fact that WASM-MUTATE synthesizes variants that effectively alter memory access patterns. We have identified four primary factors responsible for the divergence in memory accesses among WASM-MUTATE generated variants. First, modifications to the binary layout—even those that do not affect executed code—inevitably alter memory accesses within the program’s stack. Specifically, WASM-MUTATE generates variants that modify the return addresses of functions, which consequently leads to differences in execution flow and memory accesses. Second, one of our rewriting rules incorporates artificial global values into WebAssembly binaries. The access to these global variables inevitably affects the managed memory (see Section 2.1.3). Third, WASM-MUTATE injects ‘phantom’ instructions which do not aim to modify the outcome of a transformed function during execution. These intermediate calculations trigger the spill/reload component of the wasmtime compiler, varying spill and reload operations. In the context of limited physical resources, these operations temporarily store values in memory for later retrieval and use, thus creating diverse managed memory accesses (see the example at Section 3.3.1). Finally, certain rewriting rules implemented by WASM-MUTATE replicate fragments of code, e.g., performing commutative operations. These code segments may contain memory accesses, and while neither the memory addresses nor their values change, the frequency of these operations does.
Managed memory impact: The success in diminishing Spectre attacks is mainly explained by the fact that WASM-MUTATE synthesizes variants that effectively alter memory access patterns. We have identified four primary factors responsible for the divergence in memory accesses among WASM-MUTATE generated variants. First, modifications to the binary layout—even those that do not affect executed code—inevitably alter memory accesses within the program’s stack. Specifically, WASM-MUTATE generates variants that modify the return addresses of functions, which consequently leads to differences in execution flow and memory accesses. Second, one of our rewriting rules incorporates artificial global values into WebAssembly binaries. The access to these global variables inevitably affects the managed memory (see Section 2.1.3). Third, WASM-MUTATE injects ‘phantom’ instructions which do not aim to modify the outcome of a transformed function during execution. These intermediate calculations trigger the spill/reload component of the wasmtime compiler, varying spill and reload operations. In the context of limited physical resources, these operations temporarily store values in memory for later retrieval and use, thus creating diverse managed memory accesses (see the example at Section 3.3.1). Finally, certain rewriting rules implemented by WASM-MUTATE replicate fragments of code, e.g., performing commutative operations. These code segments may contain memory accesses, and while neither the memory addresses nor their values change, the frequency of these operations does.
Diversification
replace 'Diversification' by 'diversification'
In this chapter, we explore Offensive and Defensive Software Diversification applied to WebAssembly. Offensive Software Diversification highlights both the potential and the latent security risks in applying Software Diversification to WebAssembly malware. Our findings suggest potential enhancements to the automatic detection of cryptojacking malware in WebAssembly, e.g., by stressing their resilience with WebAssembly malware variants. Conversely, defensive software diversification serves as a proactive guard, specifically designed to mitigate the risks associated with Spectre attacks.
In this chapter, we explore Offensive and Defensive Software Diversification applied to WebAssembly. Offensive Software Diversification highlights both the potential and the latent security risks in applying Software Diversification to WebAssembly malware. Our findings suggest potential enhancements to the automatic detection of cryptojacking malware in WebAssembly, e.g., by stressing their resilience with WebAssembly malware variants. Conversely, Defensive Software Diversification serves as a proactive guard, specifically designed to mitigate the risks associated with Spectre attacks.
Software
replace 'Software' by 'software'
In this chapter, we explore Offensive and Defensive Software Diversification applied to WebAssembly. Offensive Software Diversification highlights both the potential and the latent security risks in applying Software Diversification to WebAssembly malware. Our findings suggest potential enhancements to the automatic detection of cryptojacking malware in WebAssembly, e.g., by stressing their resilience with WebAssembly malware variants. Conversely, defensive software diversification serves as a proactive guard, specifically designed to mitigate the risks associated with Spectre attacks.
In this chapter, we explore Offensive and Defensive Software Diversification applied to WebAssembly. Offensive Software Diversification highlights both the potential and the latent security risks in applying Software Diversification to WebAssembly malware. Our findings suggest potential enhancements to the automatic detection of cryptojacking malware in WebAssembly, e.g., by stressing their resilience with WebAssembly malware variants. Conversely, Defensive Software Diversification serves as a proactive guard, specifically designed to mitigate the risks associated with Spectre attacks.
Defensive
replace 'Defensive' by 'defensive'
In this chapter, we explore Offensive and Defensive Software Diversification applied to WebAssembly. Offensive Software Diversification highlights both the potential and the latent security risks in applying Software Diversification to WebAssembly malware. Our findings suggest potential enhancements to the automatic detection of cryptojacking malware in WebAssembly, e.g., by stressing their resilience with WebAssembly malware variants. Conversely, defensive software diversification serves as a proactive guard, specifically designed to mitigate the risks associated with Spectre attacks.
In this chapter, we explore Offensive and Defensive Software Diversification applied to WebAssembly. Offensive Software Diversification highlights both the potential and the latent security risks in applying Software Diversification to WebAssembly malware. Our findings suggest potential enhancements to the automatic detection of cryptojacking malware in WebAssembly, e.g., by stressing their resilience with WebAssembly malware variants. Conversely, Defensive Software Diversification serves as a proactive guard, specifically designed to mitigate the risks associated with Spectre attacks.
Conclusions
replace 'Conclusions' by 'Conclusions.'
Conclusions.
Conclusions
contributions
replace 'contributions' by 'contributions.'
Summary of technical contributions.
Summary of technical contributions
This
replace 'This' by 'thing.'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
techniques.
replace 'techniques.' by 'good'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
hardening
replace 'hardening' by 'a'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
requires
replace 'requires' by 'is'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
of
replace 'of' by 'techniques.'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
adoption
replace 'adoption' by 'hardening'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
growing
replace 'growing' by 'requires'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
‘i
replace '‘i' by 'WebAssembly'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
Diversification.
replace 'Diversification.' by 'of'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
Software
replace 'Software' by 'adoption'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
in
replace 'in' by '‘i'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
directions
replace 'directions' by 'Diversification.'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
research
replace 'research' by 'Software'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
future
replace 'future' by 'WebAssembly'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
discuss
replace 'discuss' by 'in'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
we
replace 'we' by 'directions'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
Finally,
replace 'Finally,' by 'research'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
research.
replace 'research.' by 'future'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
our
replace 'our' by 'discuss'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
of
replace 'of' by 'we'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
findings
replace 'findings' by 'Finally,'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
empirical
replace 'empirical' by 'research.'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
the
replace 'the' by 'our'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
overview
delete 'overview' by ''
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
an
delete 'an' by ''
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
including
replace 'including' by 'findings'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
dissertation,
replace 'dissertation,' by 'empirical'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
this
replace 'this' by 'the'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
insert '' by 'overview'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
contributions
replace 'contributions' by 'an'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
technical
replace 'technical' by 'including'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
the
replace 'the' by 'dissertation,'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
summarize
replace 'summarize' by 'this'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
we
replace 'we' by 'of'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
chapter,
replace 'chapter,' by 'contributions'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
this
replace 'this' by 'technical'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
In
replace 'In' by 'the'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
programs.
replace 'programs.' by 'summarize'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
for
replace 'for' by 'chapter,'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
created
replace 'created' by 'this'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
diversification
replace 'diversification' by 'In'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
the
replace 'the' by 'programs.'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
exploiting
replace 'exploiting' by 'WebAssembly'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
insert '' by 'created'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
insert '' by 'diversification'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
cases
replace 'cases' by 'the'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
use
replace 'use' by 'exploiting'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
two
replace 'two' by 'for'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
present
replace 'present' by 'cases'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
we
replace 'we' by 'use'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
Additionally,
replace 'Additionally,' by 'two'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
WASM-MUTATE.
replace 'WASM-MUTATE.' by 'present'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
and
replace 'and' by 'we'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
MEWE,
replace 'MEWE,' by 'Additionally,'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
CROW,
replace 'CROW,' by 'WASM-MUTATE.'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
dissertation:
replace 'dissertation:' by 'and'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
this
replace 'this' by 'MEWE,'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
in
replace 'in' by 'CROW,'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
contributions
replace 'contributions' by 'dissertation:'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
technical
replace 'technical' by 'this'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
three
replace 'three' by 'in'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
introduce
replace 'introduce' by 'contributions'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
We
replace 'We' by 'technical'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
WebAssembly.
replace 'WebAssembly.' by 'three'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
for
replace 'for' by 'introduce'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
Diversification
replace 'Diversification' by 'We'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
Software
replace 'Software' by 'WebAssembly.'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
insert '' by 'Diversification'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
insert '' by 'Software'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
insert '' by 'for'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
insert '' by 'This'
This thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This is a good thing.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
thesis
This sentence does not start with an uppercase letter.
thesis contributes to this effort with a comprehensive set of methods and tools for Software Diversification for WebAssembly. We introduce three technical contributions in this dissertation: CROW, MEWE, and WASM-MUTATE. Additionally, we present two use cases for exploiting the diversification created for WebAssembly programs. In this chapter, we summarize the technical contributions of this dissertation, including an overview of the empirical findings of our research. Finally, we discuss future research directions in WebAssembly Software Diversification. ‘i growing adoption of WebAssembly requires hardening techniques. This
Knuth
replace 'Knuth' by 'Knuth.'
— Donald Knuth.
— Donald Knuth
WORK
replace 'WORK' by 'WORK.'
CONCLUSIONS AND FUTURE WORK.
CONCLUSIONS AND FUTURE WORK
CROW,
replace 'CROW,' by 'Crow,'
We have developed three open-source tools that are publicly accessible: Crow, MEWE, and WASM-MUTATE. CROW and MEWE explore a compiler-based approach, whereas WASM-MUTATE employs a method based on binary. These tools automate the process of diversification, thereby increasing their practicality for deployment. At present, WASM-MUTATE is integrated into the wasmtime project! to improve testing. Our tools are complementary, providing combined utility. For instance, when the source code for a WebAssembly binary is unavailable, WASM-MUTATE offers an efficient solution for the generation of code variants. On the other hand, CROW and MEWE are particularly suited for scenarios that require a high level of variant preservation. Finally, one can use CROW and MEWE to generate a set of variants, which can then serve as rewriting rules for WASM-MUTATE. Moreover, when practitioners need to quickly generate variants, they could employ WASM-MUTATE, despite a potential decrease in the preservation of variants.
We have developed three open-source tools that are publicly accessible: CROW, MEWE, and WASM-MUTATE. CROW and MEWE explore a compiler-based approach, whereas WASM-MUTATE employs a method based on binary. These tools automate the process of diversification, thereby increasing their practicality for deployment. At present, WASM-MUTATE is integrated into the wasmtime project! to improve testing. Our tools are complementary, providing combined utility. For instance, when the source code for a WebAssembly binary is unavailable, WASM-MUTATE offers an efficient solution for the generation of code variants. On the other hand, CROW and MEWE are particularly suited for scenarios that require a high level of variant preservation. Finally, one can use CROW and MEWE to generate a set of variants, which can then serve as rewriting rules for WASM-MUTATE. Moreover, when practitioners need to quickly generate variants, they could employ WASM-MUTATE, despite a potential decrease in the preservation of variants.
lhttps
This sentence does not start with an uppercase letter.
lhttps://github.com/bytecodealliance/wasm-tools
thesis
replace 'thesis' by 'thesis.'
Key results of the thesis.
Key results of the thesis
Work
replace 'Work' by 'Work.'
Future Work.
Future Work
Diversification
replace 'Diversification' by 'diversification'
Remarkably, we ensure the rapid transformation of WebAssembly binaries, creating thousands of variants in a matter of minutes. This swift generation of variants is particularly advantageous in highly dynamic scenarios such as FaaS and CDN platforms. In this work, we do not discuss this case in depth. Yet, we have empirically tested the effectiveness of moving target defense techniques [37]. These tests were conducted on the Fastly edge computing platform. In this scenario, we incorporate multivariant executions [37]. Fastly can redeploy a WebAssembly binary across its 73 data centers worldwide in 13 seconds on average. This enables the practical deployment of a unique variant per node using our tools. However, a 13-second window may still pose a risk despite each node potentially hosting a distinct WebAssembly variant. To mitigate this, one could use multivariant binaries, invoking a unique variant every time the node is invoked. Our tools can generate dozens of unique variants every few seconds, each serving as a multivariant binary packaging thousands of other variants. This illustrates the real-world application of defensive software diversification to a WebAssembly standalone scenario.
Remarkably, we ensure the rapid transformation of WebAssembly binaries, creating thousands of variants in a matter of minutes. This swift generation of variants is particularly advantageous in highly dynamic scenarios such as FaaS and CDN platforms. In this work, we do not discuss this case in depth. Yet, we have empirically tested the effectiveness of moving target defense techniques [37]. These tests were conducted on the Fastly edge computing platform. In this scenario, we incorporate multivariant executions [37]. Fastly can redeploy a WebAssembly binary across its 73 data centers worldwide in 13 seconds on average. This enables the practical deployment of a unique variant per node using our tools. However, a 13-second window may still pose a risk despite each node potentially hosting a distinct WebAssembly variant. To mitigate this, one could use multivariant binaries, invoking a unique variant every time the node is invoked. Our tools can generate dozens of unique variants every few seconds, each serving as a multivariant binary packaging thousands of other variants. This illustrates the real-world application of Defensive Software Diversification to a WebAssembly standalone scenario.
Software
replace 'Software' by 'software'
Remarkably, we ensure the rapid transformation of WebAssembly binaries, creating thousands of variants in a matter of minutes. This swift generation of variants is particularly advantageous in highly dynamic scenarios such as FaaS and CDN platforms. In this work, we do not discuss this case in depth. Yet, we have empirically tested the effectiveness of moving target defense techniques [37]. These tests were conducted on the Fastly edge computing platform. In this scenario, we incorporate multivariant executions [37]. Fastly can redeploy a WebAssembly binary across its 73 data centers worldwide in 13 seconds on average. This enables the practical deployment of a unique variant per node using our tools. However, a 13-second window may still pose a risk despite each node potentially hosting a distinct WebAssembly variant. To mitigate this, one could use multivariant binaries, invoking a unique variant every time the node is invoked. Our tools can generate dozens of unique variants every few seconds, each serving as a multivariant binary packaging thousands of other variants. This illustrates the real-world application of defensive software diversification to a WebAssembly standalone scenario.
Remarkably, we ensure the rapid transformation of WebAssembly binaries, creating thousands of variants in a matter of minutes. This swift generation of variants is particularly advantageous in highly dynamic scenarios such as FaaS and CDN platforms. In this work, we do not discuss this case in depth. Yet, we have empirically tested the effectiveness of moving target defense techniques [37]. These tests were conducted on the Fastly edge computing platform. In this scenario, we incorporate multivariant executions [37]. Fastly can redeploy a WebAssembly binary across its 73 data centers worldwide in 13 seconds on average. This enables the practical deployment of a unique variant per node using our tools. However, a 13-second window may still pose a risk despite each node potentially hosting a distinct WebAssembly variant. To mitigate this, one could use multivariant binaries, invoking a unique variant every time the node is invoked. Our tools can generate dozens of unique variants every few seconds, each serving as a multivariant binary packaging thousands of other variants. This illustrates the real-world application of Defensive Software Diversification to a WebAssembly standalone scenario.
Defensive
replace 'Defensive' by 'defensive'
Remarkably, we ensure the rapid transformation of WebAssembly binaries, creating thousands of variants in a matter of minutes. This swift generation of variants is particularly advantageous in highly dynamic scenarios such as FaaS and CDN platforms. In this work, we do not discuss this case in depth. Yet, we have empirically tested the effectiveness of moving target defense techniques [37]. These tests were conducted on the Fastly edge computing platform. In this scenario, we incorporate multivariant executions [37]. Fastly can redeploy a WebAssembly binary across its 73 data centers worldwide in 13 seconds on average. This enables the practical deployment of a unique variant per node using our tools. However, a 13-second window may still pose a risk despite each node potentially hosting a distinct WebAssembly variant. To mitigate this, one could use multivariant binaries, invoking a unique variant every time the node is invoked. Our tools can generate dozens of unique variants every few seconds, each serving as a multivariant binary packaging thousands of other variants. This illustrates the real-world application of defensive software diversification to a WebAssembly standalone scenario.
Remarkably, we ensure the rapid transformation of WebAssembly binaries, creating thousands of variants in a matter of minutes. This swift generation of variants is particularly advantageous in highly dynamic scenarios such as FaaS and CDN platforms. In this work, we do not discuss this case in depth. Yet, we have empirically tested the effectiveness of moving target defense techniques [37]. These tests were conducted on the Fastly edge computing platform. In this scenario, we incorporate multivariant executions [37]. Fastly can redeploy a WebAssembly binary across its 73 data centers worldwide in 13 seconds on average. This enables the practical deployment of a unique variant per node using our tools. However, a 13-second window may still pose a risk despite each node potentially hosting a distinct WebAssembly variant. To mitigate this, one could use multivariant binaries, invoking a unique variant every time the node is invoked. Our tools can generate dozens of unique variants every few seconds, each serving as a multivariant binary packaging thousands of other variants. This illustrates the real-world application of Defensive Software Diversification to a WebAssembly standalone scenario.
programs
replace 'programs' by 'programs.'
Data augmentation for Machine Learning on WebAssembly programs.
Data augmentation for Machine Learning on WebAssembly programs
insert '' by 'span.'
In the context of WebAssembly, this process presents particularly challenging aspects since it is highly dynamic [17]. For instance, within a browser context, we need to ensure the WebAssembly binary varies not only per browser instance but also per tab process (webpage tab). In the backend, we must guarantee that the WebAssembly binary is unique per cold spawn [50, 63]. Hence, it becomes necessary to ensure that the diversification process is both rapid and efficient, capable of generating a vast number of variants within a brief time span. Specifically, this entails producing millions of unique and diverse variants every second.
In the context of WebAssembly, this process presents particularly challenging aspects since it is highly dynamic [17]. For instance, within a browser context, we need to ensure the WebAssembly binary varies not only per browser instance but also per tab process (webpage tab). In the backend, we must guarantee that the WebAssembly binary is unique per cold spawn [50, 63]. Hence, it becomes necessary to ensure that the diversification process is both rapid and efficient, capable of generating a vast number of variants within a brief timespan. Specifically, this entails producing millions of unique and diverse variants every second.
timespan.
replace 'timespan.' by 'time'
In the context of WebAssembly, this process presents particularly challenging aspects since it is highly dynamic [17]. For instance, within a browser context, we need to ensure the WebAssembly binary varies not only per browser instance but also per tab process (webpage tab). In the backend, we must guarantee that the WebAssembly binary is unique per cold spawn [50, 63]. Hence, it becomes necessary to ensure that the diversification process is both rapid and efficient, capable of generating a vast number of variants within a brief time span. Specifically, this entails producing millions of unique and diverse variants every second.
In the context of WebAssembly, this process presents particularly challenging aspects since it is highly dynamic [17]. For instance, within a browser context, we need to ensure the WebAssembly binary varies not only per browser instance but also per tab process (webpage tab). In the backend, we must guarantee that the WebAssembly binary is unique per cold spawn [50, 63]. Hence, it becomes necessary to ensure that the diversification process is both rapid and efficient, capable of generating a vast number of variants within a brief timespan. Specifically, this entails producing millions of unique and diverse variants every second.
insert '' by 'shot”'
Oneshot diversification denotes the automatic creation of a unique software program variant with each installation or distribution. This procedure entails systematic alterations to the original program. The objective is to ensure that each software copy is distinctive from all others. Contrary to randomization, one-shot diversification usually happens during the software’s distribution or installation phase. The term “one shot” describes the diversification’s static nature following its one-time implementation per installation. Once used, the diversified program is discarded. In summary, oneshot diversification bolsters security and heightens reliability by diminishing the predictability of software. We therefore plan to investigate the feasibility of one-shot diversification in WebAssembly. However, we foresee several challenges, particularly the optimization of our previously presented tools.
Oneshot diversification denotes the automatic creation of a unique software program variant with each installation or distribution. This procedure entails systematic alterations to the original program. The objective is to ensure that each software copy is distinctive from all others. Contrary to randomization, oneshot diversification usually happens during the software’s distribution or installation phase. The term “oneshot” describes the diversification’s static nature following its one-time implementation per installation. Once used, the diversified program is discarded. In summary, oneshot diversification bolsters security and heightens reliability by diminishing the predictability of software. We therefore plan to investigate the feasibility of one-shot diversification in WebAssembly. However, we foresee several challenges, particularly the optimization of our previously presented tools.
“oneshot”
replace '“oneshot”' by '“one'
Oneshot diversification denotes the automatic creation of a unique software program variant with each installation or distribution. This procedure entails systematic alterations to the original program. The objective is to ensure that each software copy is distinctive from all others. Contrary to randomization, one-shot diversification usually happens during the software’s distribution or installation phase. The term “one shot” describes the diversification’s static nature following its one-time implementation per installation. Once used, the diversified program is discarded. In summary, oneshot diversification bolsters security and heightens reliability by diminishing the predictability of software. We therefore plan to investigate the feasibility of one-shot diversification in WebAssembly. However, we foresee several challenges, particularly the optimization of our previously presented tools.
Oneshot diversification denotes the automatic creation of a unique software program variant with each installation or distribution. This procedure entails systematic alterations to the original program. The objective is to ensure that each software copy is distinctive from all others. Contrary to randomization, oneshot diversification usually happens during the software’s distribution or installation phase. The term “oneshot” describes the diversification’s static nature following its one-time implementation per installation. Once used, the diversified program is discarded. In summary, oneshot diversification bolsters security and heightens reliability by diminishing the predictability of software. We therefore plan to investigate the feasibility of one-shot diversification in WebAssembly. However, we foresee several challenges, particularly the optimization of our previously presented tools.
oneshot
replace 'oneshot' by 'one-shot'
Oneshot diversification denotes the automatic creation of a unique software program variant with each installation or distribution. This procedure entails systematic alterations to the original program. The objective is to ensure that each software copy is distinctive from all others. Contrary to randomization, one-shot diversification usually happens during the software’s distribution or installation phase. The term “one shot” describes the diversification’s static nature following its one-time implementation per installation. Once used, the diversified program is discarded. In summary, oneshot diversification bolsters security and heightens reliability by diminishing the predictability of software. We therefore plan to investigate the feasibility of one-shot diversification in WebAssembly. However, we foresee several challenges, particularly the optimization of our previously presented tools.
Oneshot diversification denotes the automatic creation of a unique software program variant with each installation or distribution. This procedure entails systematic alterations to the original program. The objective is to ensure that each software copy is distinctive from all others. Contrary to randomization, oneshot diversification usually happens during the software’s distribution or installation phase. The term “oneshot” describes the diversification’s static nature following its one-time implementation per installation. Once used, the diversified program is discarded. In summary, oneshot diversification bolsters security and heightens reliability by diminishing the predictability of software. We therefore plan to investigate the feasibility of one-shot diversification in WebAssembly. However, we foresee several challenges, particularly the optimization of our previously presented tools.
Diversification
replace 'Diversification' by 'Diversification.'
Oneshot Diversification.
Oneshot Diversification
insert '' by 'Object-Oriented'
[3] A. Guha, C. Saftoiu, and S. Krishnamurthi, “The Essence of JavaScript,” in ECOOP 2010 - Object-Oriented Programming, vol. 6183, pp. 126-150, 2010.
[3] A. Guha, C. Saftoiu, and S. Krishnamurthi, “The Essence of JavaScript,” in ECOOP 2010 -Object-Oriented Programming, vol. 6183, pp. 126-150, 2010.
-Object-Oriented
replace '-Object-Oriented' by '-'
[3] A. Guha, C. Saftoiu, and S. Krishnamurthi, “The Essence of JavaScript,” in ECOOP 2010 - Object-Oriented Programming, vol. 6183, pp. 126-150, 2010.
[3] A. Guha, C. Saftoiu, and S. Krishnamurthi, “The Essence of JavaScript,” in ECOOP 2010 -Object-Oriented Programming, vol. 6183, pp. 126-150, 2010.
eople/Berners-Lee/WorldWideWeb.html,
delete 'eople/Berners-Lee/WorldWideWeb.html,' by ''
[2] Tim Berners-Lee, “The WorldWideWeb Browser.” https://www.w3.org/People/Berners-Lee/WorldWideWeb.html, 1990.
[2] Tim Berners-Lee, “The WorldWideWeb Browser.” https://www.w3.org/P eople/Berners-Lee/WorldWideWeb.html, 1990.
https://www.w3.org/P
replace 'https://www.w3.org/P' by 'https://www.w3.org/People/Berners-Lee/WorldWideWeb.html,'
[2] Tim Berners-Lee, “The WorldWideWeb Browser.” https://www.w3.org/People/Berners-Lee/WorldWideWeb.html, 1990.
[2] Tim Berners-Lee, “The WorldWideWeb Browser.” https://www.w3.org/P eople/Berners-Lee/WorldWideWeb.html, 1990.
abs/1912.02285,
replace 'abs/1912.02285,' by 'Abs/1912.02285,'
[9] S. Narayan, T. Garfinkel, S. Lerner, H. Shacham, and D. Stefan, “Gobi: WebAssembly as a Practical Path to Library Sandboxing,” CoRR, vol. Abs/1912.02285, 2019.
[9] S. Narayan, T. Garfinkel, S. Lerner, H. Shacham, and D. Stefan, “Gobi: WebAssembly as a Practical Path to Library Sandboxing,” CoRR, vol. abs/1912.02285, 2019.
“metaSafer:
replace '“metaSafer:' by '“MetaSafer:'
[19] S. Song, S. Park, and D. Kwon, “MetaSafer: A Technique to Detect Heap Metadata Corruption in WebAssembly,” IEEE Access, vol. 11, pp. 124887-— 124898, 2023.
[19] S. Song, S. Park, and D. Kwon, “metaSafer: A Technique to Detect Heap Metadata Corruption in WebAssembly,” IEEE Access, vol. 11, pp. 124887-— 124898, 2023.
"
Unpaired symbol: ‘"’ seems to be missing
[18] T. Rokicki, C. Maurice, M. Botvinnik, and Y. Oren, “Port Contention Goes Portable: Port Contention Side Channels in Web Browsers,” in ASIA CCS "22: ACM Asia Conference on Computer and Communications Security, pp. 1182-1194, 2022.
[
Unpaired symbol: ‘]’ seems to be missing
[17] M. N. Hoque and K. A. Harras, “WebAssembly for Edge Computing: Potential and Challenges,” [EEE Communications Standards Magazine, vol. 6, no. 4, pp. 68-73, 2022.
php?title=National_Cyber_Leap_Year,
replace 'php?title=National_Cyber_Leap_Year,' by 'Year,'
[15] NSA, “National Cyber Leap Year.” https://www.nitrd.gov. gov/nitrdgroups/index. php?title=National_Cyber_Leap Year, 2021.
[15] NSA, “National Cyber Leap Year.” https://www.nitrd. gov/nitrdgroups/ index. php?title=National_Cyber_Leap_Year, 2021.
index.
replace 'index.' by 'php?title=National_Cyber_Leap'
[15] NSA, “National Cyber Leap Year.” https://www.nitrd.gov. gov/nitrdgroups/index. php?title=National_Cyber_Leap Year, 2021.
[15] NSA, “National Cyber Leap Year.” https://www.nitrd. gov/nitrdgroups/ index. php?title=National_Cyber_Leap_Year, 2021.
gov/nitrdgroups/
replace 'gov/nitrdgroups/' by 'gov/nitrdgroups/index.'
[15] NSA, “National Cyber Leap Year.” https://www.nitrd.gov. gov/nitrdgroups/index. php?title=National_Cyber_Leap Year, 2021.
[15] NSA, “National Cyber Leap Year.” https://www.nitrd. gov/nitrdgroups/ index. php?title=National_Cyber_Leap_Year, 2021.
https://www.nitrd.
replace 'https://www.nitrd.' by 'https://www.nitrd.gov.'
[15] NSA, “National Cyber Leap Year.” https://www.nitrd.gov. gov/nitrdgroups/index. php?title=National_Cyber_Leap Year, 2021.
[15] NSA, “National Cyber Leap Year.” https://www.nitrd. gov/nitrdgroups/ index. php?title=National_Cyber_Leap_Year, 2021.
gov
This sentence does not start with an uppercase letter.
[15] NSA, “National Cyber Leap Year.” https://www.nitrd. gov/nitrdgroups/ index. php?title=National_Cyber_Leap_Year, 2021.
[
Unpaired symbol: ‘]’ seems to be missing
[21 Q. Stiévenart, C. D. Roover, and M. Ghafari, “Security Risks of Porting C Programs to Webassembly,” in SAC ’22: The 87th ACM/SIGAPP Symposium on Applied Computing, pp. 1713-1722, 2022.
“RAPID:
replace '“RAPID:' by '“RAPID'
[30] J. D. P. Rodriguez and J. Posegga, “RAPID Resource and API-based Detection Against In-browser Miners,” in Proceedings of the 34th Annual Computer Security Applications Conference, ACSAC 2018, San Juan, PR, USA, December 03-07, 2018, pp. 313-326, ACM, 2018.
[30] J. D. P. Rodriguez and J. Posegga, “RAPID: Resource and API-based Detection Against In-browser Miners,” in Proceedings of the 34th Annual Computer Security Applications Conference, ACSAC 2018, San Juan, PR, USA, December 03-07, 2018, pp. 313-326, ACM, 2018.
insert '' by '28th'
[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: Secure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security - 28th European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.
[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: SEcure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security -28rd European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.
-28rd
replace '-28rd' by '-'
[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: Secure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security - 28th European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.
[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: SEcure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security -28rd European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.
SEcure
replace 'SEcure' by 'Secure'
[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: Secure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security - 28th European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.
[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: SEcure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security -28rd European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.
Hao
‘Hao’ is an imported foreign name or expression, which originally has a diacritic.
[29] W. Wang, B. Ferrell, X. Xu, K. W. Hamlen, and S. Hao, “SEISMIC: SEcure In-lined Script Monitors for Interrupting Cryptojacks,” in Computer Security -28rd European Symposium on Research in Computer Security, ESORICS, vol. 11099, pp. 122-142, 2018.
1{th
replace '1{th' by '1th'
[24] M. Musch, C. Wressnegger, M. Johns, and K. Rieck, “Thieves in the Browser: Web-based Cryptojacking in the Wild,” in Proceedings of the 1th International Conference on Availability, Reliability and Security, ARES 2019, Canterbury, UK, August 26-29, 2019, pp. 4:1-4:10, ACM, 2019.
[24] M. Musch, C. Wressnegger, M. Johns, and K. Rieck, “Thieves in the Browser: Web-based Cryptojacking in the Wild,” in Proceedings of the 1{th International Conference on Availability, Reliability and Security, ARES 2019, Canterbury, UK, August 26-29, 2019, pp. 4:1-4:10, ACM, 2019.
{
Unpaired symbol: ‘}’ seems to be missing
[24] M. Musch, C. Wressnegger, M. Johns, and K. Rieck, “Thieves in the Browser: Web-based Cryptojacking in the Wild,” in Proceedings of the 1{th International Conference on Availability, Reliability and Security, ARES 2019, Canterbury, UK, August 26-29, 2019, pp. 4:1-4:10, ACM, 2019.
Speculative
delete 'Speculative' by ''
[23] G. Maisuradze and C. Rossow, “Speculative Execution Using Return Stack Buffers,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS, pp. 2109-2122, 2018.
[23] G. Maisuradze and C. Rossow, “ret2spec: Speculative Execution Using Return Stack Buffers,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS, pp. 2109-2122, 2018.
“ret2spec:
replace '“ret2spec:' by '“Speculative'
[23] G. Maisuradze and C. Rossow, “Speculative Execution Using Return Stack Buffers,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS, pp. 2109-2122, 2018.
[23] G. Maisuradze and C. Rossow, “ret2spec: Speculative Execution Using Return Stack Buffers,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS, pp. 2109-2122, 2018.
Non-web
replace 'Non-web' by 'Non-Web'
[43] B. Spies and M. Mock, “An Evaluation of WebAssembly in Non-Web Environments,” in XLVII Latin American Computing Conference, CLEI 2021, Cartago, Costa Rica, October 25-29, 2021, pp. 1-10, IKEE, 2021.
[43] B. Spies and M. Mock, “An Evaluation of WebAssembly in Non-web Environments,” in XLVII Latin American Computing Conference, CLEI 2021, Cartago, Costa Rica, October 25-29, 2021, pp. 1-10, IKEE, 2021.
lve?urn=urn:nbn:se:kth:diva-317331.
delete 'lve?urn=urn:nbn:se:kth:diva-317331.' by ''
[40] J. Cabrera Arteaga, “Artificial Software Diversification for WebAssembly,” No. 2022:52 in TRITA-EECS-AVL, p. 112, 2022. https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-317331.
[40] J. Cabrera Arteaga, “Artificial Software Diversification for WebAssembly,” No. 2022:52 in TRITA-EECS-AVL, p. 112, 2022. https://urn.kb.se/reso lve?urn=urn:nbn:se:kth:diva-317331.
https://urn.kb.se/reso
replace 'https://urn.kb.se/reso' by 'https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-317331.'
[40] J. Cabrera Arteaga, “Artificial Software Diversification for WebAssembly,” No. 2022:52 in TRITA-EECS-AVL, p. 112, 2022. https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-317331.
[40] J. Cabrera Arteaga, “Artificial Software Diversification for WebAssembly,” No. 2022:52 in TRITA-EECS-AVL, p. 112, 2022. https://urn.kb.se/reso lve?urn=urn:nbn:se:kth:diva-317331.
Malivitsis,
replace 'Malivitsis,' by 'Malivitis,'
[36] J.C. Arteaga, O. F. Malivitis, O. L. V. Pérez, B. Baudry, and M. Monperrus, “Crow: Code diversification for webassembly,” in Proceedings of MadWEB, NDSS, 2021.
[36] J.C. Arteaga, O. F. Malivitsis, O. L. V. Pérez, B. Baudry, and M. Monperrus, “Crow: Code diversification for webassembly,” in Proceedings of MadWEB, NDSS, 2021.
Inst
replace 'Inst' by 'Institute'
[32] H. Okhravi, M. Rabe, T. Mayberry, W. Leonard, T. Hobson, D. Bigelow, and W. Streilein, “Survey of Cyber Moving Targets,” Massachusetts Institute of Technology Lexington Lincoln Lab, No. MIT/LL-TR-1166, 2013.
[32] H. Okhravi, M. Rabe, T. Mayberry, W. Leonard, T. Hobson, D. Bigelow, and W. Streilein, “Survey of Cyber Moving Targets,” Massachusetts Inst of Technology Lexington Lincoln Lab, No. MIT/LL-TR-1166, 2013.
fiir
replace 'fiir' by 'for'
[54] F. Marques, J. Fragoso Santos, N. Santos, and P. Adao, “Concolic Execution for WebAssembly,” Schloss Dagstuhl-Leibniz-Zentrum for Informatik, 2022.
[54] F. Marques, J. Fragoso Santos, N. Santos, and P. Adao, “Concolic Execution for WebAssembly,” Schloss Dagstuhl-Leibniz-Zentrum fiir Informatik, 2022.
Comput.
replace 'Comput.' by 'Computer.'
[53] T. Brito, P. Lopes, N. Santos, and J. F. Santos, “Wasmati: An efficient static vulnerability scanner for WebAssembly,” Computer. Secur., vol. 118, p. 102745, 2022.
[53] T. Brito, P. Lopes, N. Santos, and J. F. Santos, “Wasmati: An efficient static vulnerability scanner for WebAssembly,” Comput. Secur., vol. 118, p. 102745, 2022.
103,
delete '103,' by ''
[52] I. Bastys, M. Algehed, A. Sjésten, and A. Sabelfeld, “SecWasm Information Flow Control for WebAssembly,” in Static Analysis -29th International Symposium, SAS, vol. 13790 of Lecture Notes in Computer Science, pp. 74—103, Springer, 2022.
[52] I. Bastys, M. Algehed, A. Sjésten, and A. Sabelfeld, “SecWasm: Information Flow Control for WebAssembly,” in Static Analysis -29th International Symposium, SAS, vol. 13790 of Lecture Notes in Computer Science, pp. 74— 103, Springer, 2022.
74—
replace '74—' by '74—103,'
[52] I. Bastys, M. Algehed, A. Sjésten, and A. Sabelfeld, “SecWasm Information Flow Control for WebAssembly,” in Static Analysis -29th International Symposium, SAS, vol. 13790 of Lecture Notes in Computer Science, pp. 74—103, Springer, 2022.
[52] I. Bastys, M. Algehed, A. Sjésten, and A. Sabelfeld, “SecWasm: Information Flow Control for WebAssembly,” in Static Analysis -29th International Symposium, SAS, vol. 13790 of Lecture Notes in Computer Science, pp. 74— 103, Springer, 2022.
“SecWasm:
replace '“SecWasm:' by '“SecWasm'
[52] I. Bastys, M. Algehed, A. Sjésten, and A. Sabelfeld, “SecWasm Information Flow Control for WebAssembly,” in Static Analysis -29th International Symposium, SAS, vol. 13790 of Lecture Notes in Computer Science, pp. 74—103, Springer, 2022.
[52] I. Bastys, M. Algehed, A. Sjésten, and A. Sabelfeld, “SecWasm: Information Flow Control for WebAssembly,” in Static Analysis -29th International Symposium, SAS, vol. 13790 of Lecture Notes in Computer Science, pp. 74— 103, Springer, 2022.
loT
replace 'loT' by 'LoT'
[49] N. Makitalo, T. Mikkonen, C. Pautasso, V. Bankowski, P. Daubaris, R. Mikkola, and O. Beletski, “WebAssembly Modules as Lightweight Containers for Liquid LoT Applications,” in Proceedings of Web Engineering -21st International Conference, ICWE, vol. 12706, pp. 328-336, 2021.
[49] N. Makitalo, T. Mikkonen, C. Pautasso, V. Bankowski, P. Daubaris, R. Mikkola, and O. Beletski, “WebAssembly Modules as Lightweight Containers for Liquid loT Applications,” in Proceedings of Web Engineering -21st International Conference, ICWE, vol. 12706, pp. 328-336, 2021.
arXiv:2305.13241,
replace 'arXiv:2305.13241,' by 'ArXiv:2305.13241,'
[48] B. L. Titzer, “Whose baseline compiler is it anyway?,” arXiv e-prints, p. ArXiv:2305.13241, May 2023.
[48] B. L. Titzer, “Whose baseline compiler is it anyway?,” arXiv e-prints, p. arXiv:2305.13241, May 2023.
Applications,”
replace 'Applications,”' by 'Applications,'
[46] Y. Yan, T. Tu, L. Zhao, Y. Zhou, and W. Wang, Understanding the Performance of Webassembly Applications, in Proceedings of the 21st ACM Internet Measurement Conference, IMC ’21, p. 533-549, 2021.
[46] Y. Yan, T. Tu, L. Zhao, Y. Zhou, and W. Wang, “Understanding the Performance of Webassembly Applications,” in Proceedings of the 21st ACM Internet Measurement Conference, IMC ’21, p. 533-549, 2021.
“Understanding
replace '“Understanding' by 'Understanding'
[46] Y. Yan, T. Tu, L. Zhao, Y. Zhou, and W. Wang, Understanding the Performance of Webassembly Applications, in Proceedings of the 21st ACM Internet Measurement Conference, IMC ’21, p. 533-549, 2021.
[46] Y. Yan, T. Tu, L. Zhao, Y. Zhou, and W. Wang, “Understanding the Performance of Webassembly Applications,” in Proceedings of the 21st ACM Internet Measurement Conference, IMC ’21, p. 533-549, 2021.
insert '' by 'transitions'
[64] M. Kolosick, 5. Narayan, E. Johnson, C. Watt, M. LeMay, D. Garg, R. Jhala, and D. Stefan, “Isolation Without Taxation: Near Zero-cost transitions for WebAssembly And SFI,” Proc. ACM Program. Lang., vol. 6, no. POPL, pp. 1-30, 2022.
[64] M. Kolosick, 5. Narayan, E. Johnson, C. Watt, M. LeMay, D. Garg, R. Jhala, and D. Stefan, “Isolation Without Taxation: Near-Zero-cost Transitions for WebAssembly And SFI,” Proc. ACM Program. Lang., vol. 6, no. POPL, pp. 1-30, 2022.
Transitions
replace 'Transitions' by 'Zero-cost'
[64] M. Kolosick, 5. Narayan, E. Johnson, C. Watt, M. LeMay, D. Garg, R. Jhala, and D. Stefan, “Isolation Without Taxation: Near Zero-cost transitions for WebAssembly And SFI,” Proc. ACM Program. Lang., vol. 6, no. POPL, pp. 1-30, 2022.
[64] M. Kolosick, 5. Narayan, E. Johnson, C. Watt, M. LeMay, D. Garg, R. Jhala, and D. Stefan, “Isolation Without Taxation: Near-Zero-cost Transitions for WebAssembly And SFI,” Proc. ACM Program. Lang., vol. 6, no. POPL, pp. 1-30, 2022.
Near-Zero-cost
replace 'Near-Zero-cost' by 'Near'
[64] M. Kolosick, 5. Narayan, E. Johnson, C. Watt, M. LeMay, D. Garg, R. Jhala, and D. Stefan, “Isolation Without Taxation: Near Zero-cost transitions for WebAssembly And SFI,” Proc. ACM Program. Lang., vol. 6, no. POPL, pp. 1-30, 2022.
[64] M. Kolosick, 5. Narayan, E. Johnson, C. Watt, M. LeMay, D. Garg, R. Jhala, and D. Stefan, “Isolation Without Taxation: Near-Zero-cost Transitions for WebAssembly And SFI,” Proc. ACM Program. Lang., vol. 6, no. POPL, pp. 1-30, 2022.
abs/1802.01050,
replace 'abs/1802.01050,' by 'Abs/1802.01050,'
[59] W. Fu, R. Lin, and D. Inge, “TaintAssembly: Taint-based Information Flow Control Tracking for WebAssembly,” CoRR, vol. Abs/1802.01050, 2018.
[59] W. Fu, R. Lin, and D. Inge, “TaintAssembly: Taint-based Information Flow Control Tracking for WebAssembly,” CoRR, vol. abs/1802.01050, 2018.
abs
This sentence does not start with an uppercase letter.
[59] W. Fu, R. Lin, and D. Inge, “TaintAssembly: Taint-based Information Flow Control Tracking for WebAssembly,” CoRR, vol. abs/1802.01050, 2018.
[
Unpaired symbol: ‘]’ seems to be missing
[58] F. Breitfelder, T. Roth, L. Baumgartner, and M. Mezini, “WasmA: A Static WebAssembly Analysis Framework for Everyone,” in [EEE International Conference on Software Analysis, Evolution and Reengineering, SANER, pp. 753-757, 2023.
“WaVe:
replace '“WaVe:' by '“Wave:'
[65] E. Johnson, E. Laufer, Z. Zhao, D. Gohman, S. Narayan, S. Savage, D. Stefan, and F. Brown, “Wave: A Verifiably Secure WebAssembly Sandboxing Runtime,” in 44th IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, May 21-25, 2028, pp. 2940-2955, IEEE, 2023.
[65] E. Johnson, E. Laufer, Z. Zhao, D. Gohman, S. Narayan, S. Savage, D. Stefan, and F. Brown, “WaVe: A Verifiably Secure WebAssembly Sandboxing Runtime,” in 44th IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, May 21-25, 2028, pp. 2940-2955, IEEE, 2023.
Comput.
replace 'Comput.' by 'Computer.'
[74] T. Thiim, S. Apel, C. Kastner, I. Schaefer, and G. Saake, “A classification and survey of analysis strategies for software product lines,” ACM Computer. Surv., vol. 47, jun 2014.
[74] T. Thiim, S. Apel, C. Kastner, I. Schaefer, and G. Saake, “A classification and survey of analysis strategies for software product lines,” ACM Comput. Surv., vol. 47, jun 2014.
ART
replace 'ART' by 'Art'
[72] T. Y. Chen, F. Kuo, R. G. Merkel, and T. H. Tse, “Adaptive Random Testing: The Art of test case diversity,” J. Syst. Softw., vol. 83, no. 1, pp. 60-66, 2010.
[72] T. Y. Chen, F. Kuo, R. G. Merkel, and T. H. Tse, “Adaptive Random Testing: The ART of test case diversity,” J. Syst. Softw., vol. 83, no. 1, pp. 60-66, 2010.
’
Unpaired symbol: ‘‘’ seems to be missing
[70] S. Sidiroglou-Douskos, S. Misailovic, H. Hoffmann, and M. C. Rinard, “Managing Performance vs. Accuracy Trade-offs With Loop Perforation,” in SIGSOFT/FSE’11 19th ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE-19) and ESEC’11: 13th European Software Engineering Conference (ESEC-13), pp. 124-134, 2011.
insert '' by 'Foundations,'
[69] K. Pohl, G. Béckle, and F. van der Linden, Software Product Line Engineering - Foundations, Principles, and Techniques. Springer, 2005.
[69] K. Pohl, G. Béckle, and F. van der Linden, Software Product Line Engineering -Foundations, Principles, and Techniques. Springer, 2005.
-Foundations,
replace '-Foundations,' by '-'
[69] K. Pohl, G. Béckle, and F. van der Linden, Software Product Line Engineering - Foundations, Principles, and Techniques. Springer, 2005.
[69] K. Pohl, G. Béckle, and F. van der Linden, Software Product Line Engineering -Foundations, Principles, and Techniques. Springer, 2005.
insert '' by 'Assembly'
[66] M. Musch, C. Wressnegger, M. Johns, and K. Rieck, “New Kid on the Web: A Study on the Prevalence of Web Assembly in the Wild,” in Detection of Intrusions and Malware, and Vulnerability Assessment -16th International Conference, DIMVA, vol. 11543, pp. 23-42, 2019.
[66] M. Musch, C. Wressnegger, M. Johns, and K. Rieck, “New Kid on the Web: A Study on the Prevalence of WebAssembly in the Wild,” in Detection of Intrusions and Malware, and Vulnerability Assessment -16th International Conference, DIMVA, vol. 11543, pp. 23-42, 2019.
Diversity,”
replace 'Diversity,”' by 'diversity,”'
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
Software
replace 'Software' by 'software'
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
Dynamic
replace 'Dynamic' by 'dynamic'
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
Through
replace 'Through' by 'through'
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
Attacks
replace 'Attacks' by 'attacks'
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
Side-channel
replace 'Side-channel' by 'side-channel'
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
Cache
replace 'Cache' by 'cache'
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting cache side-channel attacks through dynamic software diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
[86] S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz, “Thwarting Cache Side-channel Attacks Through Dynamic Software Diversity,” in 22nd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 8-11, 2015, The Internet Society, 2015.
’
Unpaired symbol: ‘‘’ seems to be missing
[85] K. Pettis and R. C. Hansen, “Profile Guided Code Positioning,” in Proceedings of the ACM SIGPLAN’90 Conference on Programming Language Design and Implementation (PLDI), pp. 16-27, 1990.
abs/2308.03123,
replace 'abs/2308.03123,' by 'Abs/2308.03123,'
[96] S. Cao, N. He, Y. Guo, and H. Wang, “WASMixer: Binary Obfuscation for WebAssembly,” CoRR, vol. Abs/2308.03123, 2023.
[96] S. Cao, N. He, Y. Guo, and H. Wang, “WASMixer: Binary Obfuscation for WebAssembly,” CoRR, vol. abs/2308.03123, 2023.
abs
This sentence does not start with an uppercase letter.
[96] S. Cao, N. He, Y. Guo, and H. Wang, “WASMixer: Binary Obfuscation for WebAssembly,” CoRR, vol. abs/2308.03123, 2023.
Randomization,”
replace 'Randomization,”' by 'randomization,”'
[94] M. Chew and D. Song, “Mitigating buffer overflows by operating system randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.
[94] M. Chew and D. Song, “Mitigating Buffer Overflows by Operating System Randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.
System
replace 'System' by 'system'
[94] M. Chew and D. Song, “Mitigating buffer overflows by operating system randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.
[94] M. Chew and D. Song, “Mitigating Buffer Overflows by Operating System Randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.
Operating
replace 'Operating' by 'operating'
[94] M. Chew and D. Song, “Mitigating buffer overflows by operating system randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.
[94] M. Chew and D. Song, “Mitigating Buffer Overflows by Operating System Randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.
Overflows
replace 'Overflows' by 'overflows'
[94] M. Chew and D. Song, “Mitigating buffer overflows by operating system randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.
[94] M. Chew and D. Song, “Mitigating Buffer Overflows by Operating System Randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.
Buffer
replace 'Buffer' by 'buffer'
[94] M. Chew and D. Song, “Mitigating buffer overflows by operating system randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.
[94] M. Chew and D. Song, “Mitigating Buffer Overflows by Operating System Randomization,” Tech. Rep. CS-02-197, Carnegie Mellon University, 2002.
Secur.
delete 'Secur.' by ''
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
Dependable
replace 'Dependable' by 'Security.'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
Trans.
replace 'Trans.' by 'Dependable'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
IEEE
replace 'IEEE' by 'Trans.'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
Layout,”
replace 'Layout,”' by 'IEEE'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
Stack
replace 'Stack' by 'Layout,”'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
Randomizing
replace 'Randomizing' by 'Stack'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
by
replace 'by' by 'Randomizing'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
Violations
replace 'Violations' by 'by'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
Safety
replace 'Safety' by 'Violations'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
Memory
replace 'Memory' by 'Safety'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
Stack-based
replace 'Stack-based' by 'Memory'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
Thwarting
replace 'Thwarting' by 'Stack-based'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
“SaVioR:
replace '“SaVioR:' by 'Thwarting'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
Kang,
replace 'Kang,' by '“SaVioR:'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
B.
replace 'B.' by 'Kang,'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
insert '' by 'B.'
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Security. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
[89] S. Lee, H. Kang, J. Jang, and B. B. Kang, “SaVioR: Thwarting Stack-based Memory Safety Violations by Randomizing Stack Layout,” IEEE Trans. Dependable Secur. Comput., vol. 19, no. 4, pp. 2559-2575, 2022.
The
delete 'The' by ''
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
1997.
replace '1997.' by 'yes.'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
Zealand,
replace 'Zealand,' by 'is'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
New
replace 'New' by 'answer'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
Auckland,
replace 'Auckland,' by 'The'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
of
replace 'of' by '1997.'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
University
replace 'University' by 'Zealand,'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
Science,
replace 'Science,' by 'New'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
Computer
replace 'Computer' by 'Auckland,'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
insert '' by 'University'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
Department
replace 'Department' by 'Science,'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
rep.,
replace 'rep.,' by 'Computer'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
tech.
replace 'tech.' by 'of'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
transformations,”
replace 'transformations,”' by 'Department'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
obfuscating
replace 'obfuscating' by 'tech.'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
of
replace 'of' by 'transformations,”'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
taxonomy
replace 'taxonomy' by 'obfuscating'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
“A
replace '“A' by 'of'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
Low,
replace 'Low,' by 'taxonomy'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
D.
replace 'D.' by '“A'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
and
replace 'and' by 'Low,'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
Thomborson,
replace 'Thomborson,' by 'D.'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
C.
replace 'C.' by 'and'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
Collberg,
replace 'Collberg,' by 'Thomborson,'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
insert '' by 'Collberg,'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
insert '' by 'C.'
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. Department of Computer Science, University of Auckland, New Zealand, 1997. The answer is yes.
[97] C. Collberg, C. Thomborson, and D. Low, “A taxonomy of obfuscating transformations,” tech. rep., Department of Computer Science, University of Auckland, New Zealand, 1997. The
abs/2305.14591,
replace 'abs/2305.14591,' by 'Abs/2305.14591,'
[106] K. Zhang, D. Wang, J. Xia, W. Y. Wang, and L. Li, “ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers,” CoRR, vol. Abs/2305.14591, 2023.
[106] K. Zhang, D. Wang, J. Xia, W. Y. Wang, and L. Li, “ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers,” CoRR, vol. abs/2305.14591, 2023.
abs
This sentence does not start with an uppercase letter.
[106] K. Zhang, D. Wang, J. Xia, W. Y. Wang, and L. Li, “ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers,” CoRR, vol. abs/2305.14591, 2023.
sep
replace 'sep' by 'Sep'
[103] E. Schulte, Z. P. Fry, E. Fast, W. Weimer, and S. Forrest, “Software mutational robustness,” vol. 15, p. 281-312, Sep 2014.
[103] E. Schulte, Z. P. Fry, E. Fast, W. Weimer, and S. Forrest, “Software mutational robustness,” vol. 15, p. 281-312, sep 2014.
(101]
replace '(101]' by '(101)'
(101) B. R. Churchill, O. Padon, R. Sharma, and A. Aiken, “Semantic Program Alignment for Equivalence Checking,” in Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI, pp. 1027-1040, 2019.
(101] B. R. Churchill, O. Padon, R. Sharma, and A. Aiken, “Semantic Program Alignment for Equivalence Checking,” in Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI, pp. 1027-1040, 2019.
(
Unpaired symbol: ‘)’ seems to be missing
(101] B. R. Churchill, O. Padon, R. Sharma, and A. Aiken, “Semantic Program Alignment for Equivalence Checking,” in Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI, pp. 1027-1040, 2019.
(
Unpaired symbol: ‘)’ seems to be missing
(109] P. M. Phothilimthana, A. Thakur, R. Bodfk, and D. Dhurjati, “Scaling up Superoptimization,” in Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS, pp. 297-310, 2016.
Integr.
replace 'Integr.' by 'Integrity.'
[116] G. Agosta, A. Barenghi, G. Pelosi, and M. Scandale, “The MEET Approach: Securing Cryptographic Embedded Software Against Side Channel Attacks,” IEEE Trans. Comput. Aided Des. Integrity. Circuits Syst., vol. 34, no. 8, pp. 1820-1333, 2015.
[116] G. Agosta, A. Barenghi, G. Pelosi, and M. Scandale, “The MEET Approach: Securing Cryptographic Embedded Software Against Side Channel Attacks,” IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., vol. 34, no. 8, pp. 1820-1333, 2015.
rep.,
delete 'rep.,' by ''
[114] B. Salamat, A. Gal, T. Jackson, K. Manivannan, G. Wagner, and M. Franz, “Stopping Buffer Overflow Attacks at Run-Time: Simultaneous Multi-variant Program Execution on a Multicore Processor,” tech. Technical Report 07-13, School of Information and Computer Sciences, UCIrvine, 2007.
[114] B. Salamat, A. Gal, T. Jackson, K. Manivannan, G. Wagner, and M. Franz, “Stopping Buffer Overflow Attacks at Run-Time: Simultaneous Multi-variant Program Execution on a Multicore Processor,” tech. rep., Technical Report 07-13, School of Information and Computer Sciences, UCIrvine, 2007.
441,
delete '441,' by ''
[113] D. Bruschi, L. Cavallaro, and A. Lanzi, “Diversified Process Replicee for Defeating Memory Error Exploits,” in Proceedings of the 26th IEEE International Performance Computing and Communications Conference, IPCCC 2007, April 11-18, 2007, New Orleans, Louisiana, USA, pp. 434—441, IEEE Computer Society, 2007.
[113] D. Bruschi, L. Cavallaro, and A. Lanzi, “Diversified Process replicee for Defeating Memory Error Exploits,” in Proceedings of the 26th IEEE International Performance Computing and Communications Conference, IPCCC 2007, April 11-18, 2007, New Orleans, Louisiana, USA, pp. 434— 441, IEEE Computer Society, 2007.
434—
replace '434—' by '434—441,'
[113] D. Bruschi, L. Cavallaro, and A. Lanzi, “Diversified Process Replicee for Defeating Memory Error Exploits,” in Proceedings of the 26th IEEE International Performance Computing and Communications Conference, IPCCC 2007, April 11-18, 2007, New Orleans, Louisiana, USA, pp. 434—441, IEEE Computer Society, 2007.
[113] D. Bruschi, L. Cavallaro, and A. Lanzi, “Diversified Process replicee for Defeating Memory Error Exploits,” in Proceedings of the 26th IEEE International Performance Computing and Communications Conference, IPCCC 2007, April 11-18, 2007, New Orleans, Louisiana, USA, pp. 434— 441, IEEE Computer Society, 2007.
replicee
replace 'replicee' by 'Replicee'
[113] D. Bruschi, L. Cavallaro, and A. Lanzi, “Diversified Process Replicee for Defeating Memory Error Exploits,” in Proceedings of the 26th IEEE International Performance Computing and Communications Conference, IPCCC 2007, April 11-18, 2007, New Orleans, Louisiana, USA, pp. 434—441, IEEE Computer Society, 2007.
[113] D. Bruschi, L. Cavallaro, and A. Lanzi, “Diversified Process replicee for Defeating Memory Error Exploits,” in Proceedings of the 26th IEEE International Performance Computing and Communications Conference, IPCCC 2007, April 11-18, 2007, New Orleans, Louisiana, USA, pp. 434— 441, IEEE Computer Society, 2007.
27:13,
delete '27:13,' by ''
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
27:1—
delete '27:1—' by ''
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
pp.
replace 'pp.' by '27:1—27:13,'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
2022,
replace '2022,' by 'pp.'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
10-14,
replace '10-14,' by '2022,'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
October
replace 'October' by '10-14,'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
USA,
replace 'USA,' by 'October'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
MI,
replace 'MI,' by 'USA,'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Rochester,
replace 'Rochester,' by 'MI,'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
2022,
replace '2022,' by 'Rochester,'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
ASE
replace 'ASE' by '2022,'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Engineering,
replace 'Engineering,' by 'ASE'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Software
replace 'Software' by 'Engineering,'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Automated
replace 'Automated' by 'Software'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
on
replace 'on' by 'Automated'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Conference
replace 'Conference' by 'on'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
International
replace 'International' by 'Conference'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
IEEE/ACM
replace 'IEEE/ACM' by 'International'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
87th
replace '87th' by 'IEEE/ACM'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
in
replace 'in' by '87th'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Binaries,”
replace 'Binaries,”' by 'in'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
of
replace 'of' by 'Binaries,”'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Generation
replace 'Generation' by 'of'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Guided
replace 'Guided' by 'Generation'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Feedback
replace 'Feedback' by 'Guided'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
for
replace 'for' by 'Feedback'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Framework
replace 'Framework' by 'for'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
A
replace 'A' by 'Framework'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
:
replace ':' by 'A'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
“Cornucopia
replace '“Cornucopia' by ':'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Machiry,
replace 'Machiry,' by '“Cornucopia'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
A.
replace 'A.' by 'Machiry,'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
and
replace 'and' by 'A.'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Kulkarni,
replace 'Kulkarni,' by 'and'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
M.
replace 'M.' by 'Kulkarni,'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Saumya,
replace 'Saumya,' by 'M.'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
C.
replace 'C.' by 'Saumya,'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
Pillai,
replace 'Pillai,' by 'C.'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
A.
replace 'A.' by 'Pillai,'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
insert '' by 'A.'
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1—27:13, ACM, 2022.
[111] V. Singhal, A. A. Pillai, C. Saumya, M. Kulkarni, and A. Machiry, “Cornucopia : A Framework for Feedback Guided Generation of Binaries,” in 87th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 27:1— 27:13, ACM, 2022.
abs/2110.03301,
replace 'abs/2110.03301,' by 'Abs/2110.03301,'
[124] H. Bostani and V. Moonsamy, “EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection,” CoRR, vol. Abs/2110.03301, 2021.
[124] H. Bostani and V. Moonsamy, “EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection,” CoRR, vol. abs/2110.03301, 2021.
abs
This sentence does not start with an uppercase letter.
[124] H. Bostani and V. Moonsamy, “EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection,” CoRR, vol. abs/2110.03301, 2021.
abs/2111.11487,
replace 'abs/2111.11487,' by 'Abs/2111.11487,'
[135] P. Dasgupta and Z. Osman, “A Comparison of State-of-the-art Techniques for Generating Adversarial Malware Binaries,” CoRR, vol. Abs/2111.11487, 2021.
[135] P. Dasgupta and Z. Osman, “A Comparison of State-of-the-art Techniques for Generating Adversarial Malware Binaries,” CoRR, vol. abs/2111.11487, 2021.
abs
This sentence does not start with an uppercase letter.
[135] P. Dasgupta and Z. Osman, “A Comparison of State-of-the-art Techniques for Generating Adversarial Malware Binaries,” CoRR, vol. abs/2111.11487, 2021.
How
delete 'How' by ''
[130] R. L. Castro, C. Schmitt, and G. D. Rodosek, “How Automatic Malware Modifications Can Evade Static Detection?,” in 2019 5th International Conference on Information Management (ICIM), pp. 20-27, 2019.
[130] R. L. Castro, C. Schmitt, and G. D. Rodosek, “ARMED: How Automatic Malware Modifications Can Evade Static Detection?,” in 2019 5th International Conference on Information Management (ICIM), pp. 20-27, 2019.
“ARMED:
replace '“ARMED:' by '“How'
[130] R. L. Castro, C. Schmitt, and G. D. Rodosek, “How Automatic Malware Modifications Can Evade Static Detection?,” in 2019 5th International Conference on Information Management (ICIM), pp. 20-27, 2019.
[130] R. L. Castro, C. Schmitt, and G. D. Rodosek, “ARMED: How Automatic Malware Modifications Can Evade Static Detection?,” in 2019 5th International Conference on Information Management (ICIM), pp. 20-27, 2019.
insert '' by 'March'
[148] T. Schnitzler, K. Kohls, E. Bitsikas, and C. Pépper, “Hope of Delivery: Extracting User Locations From Mobile Instant Messengers,” in 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, February 27 - March 8, 2023, The Internet Society, 2023.
[148] T. Schnitzler, K. Kohls, E. Bitsikas, and C. Pépper, “Hope of Delivery: Extracting User Locations From Mobile Instant Messengers,” in 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, February 27 -March 8, 2023, The Internet Society, 2023.
-March
replace '-March' by '-'
[148] T. Schnitzler, K. Kohls, E. Bitsikas, and C. Pépper, “Hope of Delivery: Extracting User Locations From Mobile Instant Messengers,” in 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, February 27 - March 8, 2023, The Internet Society, 2023.
[148] T. Schnitzler, K. Kohls, E. Bitsikas, and C. Pépper, “Hope of Delivery: Extracting User Locations From Mobile Instant Messengers,” in 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, February 27 -March 8, 2023, The Internet Society, 2023.
March 8, 2023
A new year has begun. Did you mean “March 8, 2024”?
[148] T. Schnitzler, K. Kohls, E. Bitsikas, and C. Pépper, “Hope of Delivery: Extracting User Locations From Mobile Instant Messengers,” in 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, February 27 -March 8, 2023, The Internet Society, 2023.
“babble:
replace '“babble:' by '“Babble:'
[145] D. Cao, R. Kunkel, C. Nandi, M. Willsey, Z. Tatlock, and N. Polikarpova, “Babble: Learning Better Abstractions with E-Graphs and Anti-unification,” Proc. ACM Program. Lang., vol. 7, no. POPL, pp. 396-424, 2023.
[145] D. Cao, R. Kunkel, C. Nandi, M. Willsey, Z. Tatlock, and N. Polikarpova, “babble: Learning Better Abstractions with E-Graphs and Anti-unification,” Proc. ACM Program. Lang., vol. 7, no. POPL, pp. 396-424, 2023.
CoRR,
replace 'CoRR,' by 'CoRR.'
[140] R. Sasnauskas, and J. Regehr, Y. Chen, “Souper: vol. Abs/1711.04422, 2017. P. Collingbourne, J. Ketema, J. Taneja, A Synthesizing Superoptimizer,” CoRR.
[140] R. Sasnauskas, and J. Regehr, Y. Chen, “Souper: vol. abs/1711.04422, 2017. P. Collingbourne, J. Ketema, J. Taneja, A Synthesizing Superoptimizer,” CoRR,
abs/1711.04422,
replace 'abs/1711.04422,' by 'Abs/1711.04422,'
[140] R. Sasnauskas, and J. Regehr, Y. Chen, “Souper: vol. Abs/1711.04422, 2017. P. Collingbourne, J. Ketema, J. Taneja, A Synthesizing Superoptimizer,” CoRR.
[140] R. Sasnauskas, and J. Regehr, Y. Chen, “Souper: vol. abs/1711.04422, 2017. P. Collingbourne, J. Ketema, J. Taneja, A Synthesizing Superoptimizer,” CoRR,
abs
This sentence does not start with an uppercase letter.
[140] R. Sasnauskas, and J. Regehr, Y. Chen, “Souper: vol. abs/1711.04422, 2017. P. Collingbourne, J. Ketema, J. Taneja, A Synthesizing Superoptimizer,” CoRR,
Comput.
replace 'Comput.' by 'Computer.'
[150] F. Cohen, “Computer Viruses: Theory and Experiments,” Computer. Secur., vol. 6, no. 1, pp. 22-35, 1987.
[150] F. Cohen, “Computer Viruses: Theory and Experiments,” Comput. Secur., vol. 6, no. 1, pp. 22-35, 1987.
Transformations,”
replace 'Transformations,”' by 'transformations,”'
[158] W. Zhang, S. Guo, H. Zhang, Y. Sui, Y. Xue, and Y. Xu, “Challenging Machine Learning-based Clone Detectors via semantic-preserving code transformations,” IEEE Trans. Software Eng., vol. 49, no. 5, pp. 3052-3070, 2023.
[158] W. Zhang, S. Guo, H. Zhang, Y. Sui, Y. Xue, and Y. Xu, “Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations,” IEEE Trans. Software Eng., vol. 49, no. 5, pp. 3052-3070, 2023.
Code
replace 'Code' by 'code'
[158] W. Zhang, S. Guo, H. Zhang, Y. Sui, Y. Xue, and Y. Xu, “Challenging Machine Learning-based Clone Detectors via semantic-preserving code transformations,” IEEE Trans. Software Eng., vol. 49, no. 5, pp. 3052-3070, 2023.
[158] W. Zhang, S. Guo, H. Zhang, Y. Sui, Y. Xue, and Y. Xu, “Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations,” IEEE Trans. Software Eng., vol. 49, no. 5, pp. 3052-3070, 2023.
Semantic-preserving
replace 'Semantic-preserving' by 'semantic-preserving'
[158] W. Zhang, S. Guo, H. Zhang, Y. Sui, Y. Xue, and Y. Xu, “Challenging Machine Learning-based Clone Detectors via semantic-preserving code transformations,” IEEE Trans. Software Eng., vol. 49, no. 5, pp. 3052-3070, 2023.
[158] W. Zhang, S. Guo, H. Zhang, Y. Sui, Y. Xue, and Y. Xu, “Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations,” IEEE Trans. Software Eng., vol. 49, no. 5, pp. 3052-3070, 2023.
Diagnostics,”
replace 'Diagnostics,”' by 'diagnostics,”'
[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised program repair with test execution diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.
[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.
Execution
replace 'Execution' by 'execution'
[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised program repair with test execution diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.
[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.
Test
replace 'Test' by 'test'
[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised program repair with test execution diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.
[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.
Repair
replace 'Repair' by 'repair'
[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised program repair with test execution diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.
[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.
Program
replace 'Program' by 'program'
[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised program repair with test execution diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.
[157] H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “SelfAPR: Self-supervised Program Repair with Test Execution Diagnostics,” in 37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022, pp. 92:1-92:13, ACM, 2022.
arXiv:2305.19915,
replace 'arXiv:2305.19915,' by 'ArXiv:2305.19915,'
[155] T. Y. Zhuo, Z. Yang, Z. Sun, Y. Wang, L. Li, X. Du, Z. Xing, and D. Lo, “Source Code Data Augmentation for Deep Learning: A Survey,” arXiv e-prints, p. ArXiv:2305.19915, May 2023.
[155] T. Y. Zhuo, Z. Yang, Z. Sun, Y. Wang, L. Li, X. Du, Z. Xing, and D. Lo, “Source Code Data Augmentation for Deep Learning: A Survey,” arXiv e-prints, p. arXiv:2305.19915, May 2023.